diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..9531ec1
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,15 @@
+cff-version: 1.2.0
+title: Tmmax
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Bahrem Serhat
+    family-names: Danis
+    orcid: 'https://orcid.org/0009-0002-9880-0446'
+repository-code: 'https://github.com/bahremsd/tmmax'
+url: 'https://tmmax.readthedocs.io/'
+license: MIT
+version: 1.0.0
+date-released: '2025-02-01'
diff --git a/benchmarks/layer_size_benchmark.py b/benchmarks/layer_size_benchmark.py
new file mode 100644
index 0000000..4d99214
--- /dev/null
+++ b/benchmarks/layer_size_benchmark.py
@@ -0,0 +1,102 @@
+import numpy as np
+import jax
+import jax.numpy as jnp
+import timeit
+import os
+os.environ["JAX_PLATFORMS"] = "cpu"  # Force JAX to use CPU
+
+from utils import generate_material_distribution_indices
+from utils import generate_material_list_with_air
+
+from utils import generate_tmm_args, tmm_coh_tmm_wrapper
+from utils import generate_vtmm_args
+from utils import generate_tmm_fast_args
+from utils import generate_tmmax_args
+
+from tmmax.tmm import vectorized_coh_tmm
+from vtmm import tmm_rt
+from tmm_fast import coh_tmm
+
+
+# Number of layers to test from 2 to 400 (exclusive), using integer values
+number_of_layers = np.arange(2, 400, 20, dtype=int)
+
+# Number of repetitions for the timeit function to get an average execution time
+timeit_repetition = 50
+
+# List of materials used in the simulations
+material_set = ["SiO2", "TiO2", "MgF2", "MgO", "SiO", "Al2O3", "CdS"]
+
+# Polarization type, 's' polarization in this case
+polarization = 's'
+
+# Array of angles of incidence, linearly spaced from 0 to pi/2, with 20 points
+angle_of_incidences = np.linspace(0, np.pi/2, 20)
+angle_of_incidences_tmmax = jnp.array(angle_of_incidences)
+
+# Wavelength array from 500 nm to 1000 nm, linearly spaced with 20 points
+wavelength_arr = np.linspace(500e-9, 1000e-9, 20)
+wavelength_arr_tmmax = jnp.array(wavelength_arr)
+
+# Lists to store execution times for each method
+time_tmm = []
+time_vtmm = []
+time_tmm_fast = []
+time_tmmax = []
+
+# Loop through different numbers of layers
+for N in number_of_layers:
+    
+    # Generate random material distribution indices and the corresponding material list with air layers
+    indices = generate_material_distribution_indices(N, low=0, high=len(material_set))
+    material_list = generate_material_list_with_air(indices, material_set) # np.load(f"material_distribution_with_layer_num_{N}.npy", allow_pickle=True)
+    
+    # Save the material distribution list as a .npy file for reference
+    np.save(f"material_distribution_with_layer_num_{N}.npy", material_list)
+    
+    # Randomly generate thicknesses for each layer between 100 nm and 500 nm
+    thickness_list = np.random.uniform(100, 500, N) * 1e-9 # np.load(f"thickness_list_with_layer_num_{N}.npy")
+    np.save(f"thickness_list_with_layer_num_{N}.npy", thickness_list)  # Save the thickness list
+
+    thickness_list_tmm, nk_list_tmm = generate_tmm_args(material_list = material_list, 
+                                                    thickness_list = thickness_list,
+                                                    wavelength_arr = wavelength_arr)
+    
+    omega_vtmm, kx_vtmm, nk_list_vtmm, thickness_list_vtmm = generate_vtmm_args(wavelength_arr = wavelength_arr, 
+                                                                                angle_of_incidences = angle_of_incidences,
+                                                                                thickness_list = thickness_list,
+                                                                                material_list = material_list)
+    
+    t_tmm = timeit.timeit( lambda: tmm_coh_tmm_wrapper(polarization, nk_list_tmm, thickness_list_tmm, angle_of_incidences, wavelength_arr),  number=timeit_repetition )
+    t_vtmm = timeit.timeit( lambda: tmm_rt(polarization, omega_vtmm, kx_vtmm, nk_list_vtmm, thickness_list_vtmm),  number = timeit_repetition )
+
+    M_tmm_fast, T_tmm_fast = generate_tmm_fast_args(material_list = material_list, 
+                                                    thickness_list = thickness_list,
+                                                    wavelength_arr = wavelength_arr)
+
+    t_tmm_fast = timeit.timeit( lambda: coh_tmm(polarization, M_tmm_fast, T_tmm_fast, angle_of_incidences, wavelength_arr, device='cpu'),  number = timeit_repetition )
+
+
+    thickness_list_tmmax = jnp.array(thickness_list)
+    
+    data_tmmax, material_distribution_tmmax, polarization_tmmax = generate_tmmax_args(material_list = material_list, polarization = polarization)
+    
+    t_tmmax = timeit.timeit( lambda: jax.block_until_ready(vectorized_coh_tmm(data_tmmax, material_distribution_tmmax, thickness_list_tmmax, wavelength_arr_tmmax, angle_of_incidences_tmmax, polarization_tmmax)),  number = timeit_repetition)
+    
+    time_tmm.append(t_tmm)
+    time_vtmm.append(t_vtmm)
+    time_tmm_fast.append(t_tmm_fast)
+    time_tmmax.append(t_tmmax)
+
+
+
+    print(f"{N} tmm took ", t_tmm)
+    print(f"{N} vtmm took ", t_vtmm)
+    print(f"{N} tmm-fast took ", t_tmm_fast)
+    print(f"{N} tmmax took ", t_tmmax)
+
+# Save the time measurements for each method into .npy files
+np.save("time_of_tmm.npy", time_tmm)
+np.save("time_of_vtmmbur.npy", time_vtmm)
+np.save("time_of_tmm_fastbur.npy", time_tmm_fast)
+np.save("time_of_tmmaxbur.npy", time_tmmax)
\ No newline at end of file
diff --git a/benchmarks/layer_size_exp_results/layer_size_figure.png b/benchmarks/layer_size_exp_results/layer_size_figure.png
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index 0000000..47b7694
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diff --git a/benchmarks/layer_size_exp_results/layer_size_figure.svg b/benchmarks/layer_size_exp_results/layer_size_figure.svg
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index 0000000..659a723
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diff --git a/benchmarks/layer_size_exp_results/time_of_tmm.npy b/benchmarks/layer_size_exp_results/time_of_tmm.npy
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diff --git a/benchmarks/layer_size_exp_results/time_of_tmm_fast.npy b/benchmarks/layer_size_exp_results/time_of_tmm_fast.npy
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diff --git a/benchmarks/layer_size_exp_results/time_of_tmmax.npy b/benchmarks/layer_size_exp_results/time_of_tmmax.npy
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diff --git a/benchmarks/layer_size_exp_results/time_of_vtmm.npy b/benchmarks/layer_size_exp_results/time_of_vtmm.npy
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diff --git a/benchmarks/tmmax_benchmark_figures.ipynb b/benchmarks/tmmax_benchmark_figures.ipynb
new file mode 100644
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--- /dev/null
+++ b/benchmarks/tmmax_benchmark_figures.ipynb
@@ -0,0 +1,379 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Benchmarking tmm, fast-tmm, vtmm, and tmmax for Optical Thin Film Coating Design**\n",
+        "\n",
+        "In this section, we present the benchmarking results of four different transfer matrix method approach for optical thin film coating design:\n",
+        "- [tmm](https://github.com/sbyrnes321/tmm)\n",
+        "- [tmm-fast](https://github.com/MLResearchAtOSRAM/tmm_fast)\n",
+        "- [vtmm](https://github.com/fancompute/vtmm)\n",
+        "- [tmmax](https://github.com/bahremsd/tmmax)\n",
+        "\n",
+        "The goal of this benchmarking is to compare the performance of these libraries in terms of:\n",
+        "- **Computation time** as a function of **layer size**.\n",
+        "- **Computation time** as a function of **wavelength array size** and **angle of incidence array size**.\n",
+        "\n",
+        "We analyze how the number of layers in the thin film stack affects the run time and how the vectorized and optimized methods perform under varying conditions."
+      ],
+      "metadata": {
+        "id": "m5Vb3idxbVo9"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **Benchmarking Results**\n"
+      ],
+      "metadata": {
+        "id": "71dAoyPNf2wW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Run Time vs. Layer Size**"
+      ],
+      "metadata": {
+        "id": "_H6VIYOff4Pt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 388
+        },
+        "id": "UqYHKq_BbRGr",
+        "outputId": "b4b21e47-d1ba-42be-ede7-1b93a55b901e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#Load data\n",
+        "number_of_layers = np.arange(2, 400, 20, dtype=int)\n",
+        "tmm = np.load(\"time_of_tmm.npy\")\n",
+        "vtmm = np.load(\"time_of_vtmm.npy\")\n",
+        "tmm_fast = np.load(\"time_of_tmm_fast.npy\")\n",
+        "tmmax = np.load(\"time_of_tmmax.npy\")\n",
+        "\n",
+        "# Create the figure and axis\n",
+        "fig, ax = plt.subplots(figsize=(6, 4))\n",
+        "\n",
+        "# Plot three curves with labels\n",
+        "ax.plot(number_of_layers, tmm, 'o-', label=\"tmm\", linewidth=2, color='orangered')\n",
+        "ax.plot(number_of_layers, vtmm, 'o-', label=\"vtmm\", linewidth=2, color='mediumseagreen')\n",
+        "ax.plot(number_of_layers, tmm_fast, 'o-', label=\"tmm-fast\", linewidth=2, color='mediumvioletred')\n",
+        "ax.plot(number_of_layers, tmmax, 'o-', label=\"tmmax\", linewidth=2, color='navy')\n",
+        "\n",
+        "ax.set_xticks([2,100,200,300,400])\n",
+        "ax.set_yscale('log')\n",
+        "# Customize plot\n",
+        "ax.grid(True, linestyle=\"--\", alpha=0.5)\n",
+        "ax.legend()\n",
+        "ax.set_xlabel(\"Layer Size\")\n",
+        "ax.set_ylabel(\"Run Time (s)\")\n",
+        "\n",
+        "plt.savefig(\"layer_size_benchmark.svg\")\n",
+        "plt.savefig(\"layer_size_benchmark.png\", dpi = 600)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create the figure and axis\n",
+        "fig, ax = plt.subplots(figsize=(6, 4))\n",
+        "\n",
+        "# Plot three curves with labels\n",
+        "ax.plot(number_of_layers, tmm, 'o-', label=\"tmm\", linewidth=2, color='orangered')\n",
+        "ax.plot(number_of_layers, vtmm, 'o-', label=\"vtmm\", linewidth=2, color='mediumseagreen')\n",
+        "ax.plot(number_of_layers, tmm_fast, 'o-', label=\"tmm-fast\", linewidth=2, color='mediumvioletred')\n",
+        "ax.plot(number_of_layers, tmmax, 'o-', label=\"tmmax\", linewidth=2, color='navy')\n",
+        "\n",
+        "ax.set_xticks([2,7,12,17,22,27])\n",
+        "ax.set_yscale('log')\n",
+        "# Customize plot\n",
+        "ax.grid(True, linestyle=\"--\", alpha=0.5)\n",
+        "ax.set_xlabel(\"Layer Size\")\n",
+        "ax.set_ylabel(\"Run Time (s)\")\n",
+        "ax.set_xlim(0, 32)\n",
+        "ax.set_ylim(0, 2.5)\n",
+        "\n",
+        "plt.savefig(\"layer_size_benchmark_zoom.svg\")\n",
+        "plt.savefig(\"layer_size_benchmark_zoom.png\", dpi = 600)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "_UkwB8gfFl-J",
+        "outputId": "b60c42bb-0bfc-46f4-bd2c-fd5e05fb5268"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-2-5468d4fa97f2>:17: UserWarning: Attempt to set non-positive ylim on a log-scaled axis will be ignored.\n",
+            "  ax.set_ylim(0, 2.5)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Run Time** vs. **Wavelength array size** and **Angle of incidences array size**"
+      ],
+      "metadata": {
+        "id": "3GAWPeKzf9wF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Load data\n",
+        "tmm_data = np.load(\"time_of_tmm_wl_theta_arr_exp_8_layer.npy\")\n",
+        "tmm_fast_data = np.load(\"time_of_tmm_fast_wl_theta_arr_exp_8_layer.npy\")\n",
+        "vtmm_data = np.load(\"time_of_vtmm_wl_theta_arr_exp_8_layer.npy\")\n",
+        "tmmax_data = np.load(\"time_of_tmmax_wl_theta_arr_exp_8_layer.npy\")\n",
+        "\n",
+        "# Create figure and axes\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(25, 5), constrained_layout=False)\n",
+        "fig.subplots_adjust(wspace=0.35)\n",
+        "titles = ['tmm', 'tmm-fast', 'vtmm', 'tmmax']\n",
+        "\n",
+        "# Plot the first heatmap and its colorbar\n",
+        "im1 = axes[0].imshow(tmm_data, cmap='YlOrRd', aspect='equal')\n",
+        "axes[0].set_title(titles[0], fontsize=14, fontweight='bold')\n",
+        "axes[0].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[0].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[0].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[0].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[0].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[0].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[0].invert_yaxis()\n",
+        "cbar1 = fig.colorbar(im1, ax=axes[0], fraction=0.046, pad=0.04)\n",
+        "cbar1.set_label(\"Time(s)\", fontsize=10, fontweight='bold')\n",
+        "cbar1.set_ticks(np.linspace(np.min(tmm_data), np.max(tmm_data), 7, endpoint=True))\n",
+        "\n",
+        "# Plot the second heatmap and its colorbar\n",
+        "im2 = axes[1].imshow(tmm_fast_data, cmap='PuRd', aspect='equal')\n",
+        "axes[1].set_title(titles[1], fontsize=14, fontweight='bold')\n",
+        "axes[1].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[1].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[1].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[1].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[1].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[1].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[1].invert_yaxis()\n",
+        "cbar2 = fig.colorbar(im2, ax=axes[1], fraction=0.046, pad=0.04)\n",
+        "cbar2.set_label(\"Time(s)\", fontsize=10, fontweight='bold')\n",
+        "cbar2.set_ticks(np.linspace(np.min(tmm_fast_data), np.max(tmm_fast_data), 7, endpoint=True))\n",
+        "# Plot the second heatmap and its colorbar\n",
+        "im3 = axes[2].imshow(vtmm_data, cmap='PuBuGn', aspect='equal')\n",
+        "axes[2].set_title(titles[2], fontsize=14, fontweight='bold')\n",
+        "axes[2].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[2].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[2].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[2].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[2].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[2].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[2].invert_yaxis()\n",
+        "\n",
+        "# Plot the third heatmap and its colorbar\n",
+        "im4 = axes[3].imshow(tmmax_data, cmap='PuBuGn', aspect='equal')\n",
+        "axes[3].set_title(titles[3], fontsize=14, fontweight='bold')\n",
+        "axes[3].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[3].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[3].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[3].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[3].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[3].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[3].invert_yaxis()\n",
+        "# Create a shared colorbar for the second and third heatmaps\n",
+        "# Adjust the colorbar range based on the min and max values of both the second and third imshow\n",
+        "vmin = min(im3.get_clim()[0], im4.get_clim()[0])\n",
+        "vmax = max(im3.get_clim()[1], im4.get_clim()[1])\n",
+        "im3.set_clim(vmin, vmax)\n",
+        "im4.set_clim(vmin, vmax)\n",
+        "\n",
+        "# Create a shared colorbar for the second and third heatmaps\n",
+        "cbar3 = fig.colorbar(im4, ax=axes[2:], orientation='vertical', fraction=0.02, pad=0.04)\n",
+        "cbar3.set_label(\"Time(s)\", fontsize=10, fontweight='bold')\n",
+        "cbar3.set_ticks(np.linspace(np.min(np.minimum(vtmm_data, tmmax_data)), np.max(np.maximum(vtmm_data, tmmax_data)), 7, endpoint=True))\n",
+        "plt.savefig(\"wl_theta_arr_benchmark_8_layer.svg\")\n",
+        "plt.savefig(\"wl_theta_arr_benchmark_8_layer.png\", dpi = 600)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256
+        },
+        "id": "Z1jstF68f-8l",
+        "outputId": "022e5915-970a-491a-ef2f-95c5e1270e97"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2500x500 with 7 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Load data\n",
+        "tmm_data = np.load(\"time_of_tmm_wl_theta_arr_exp_80_layer.npy\")\n",
+        "tmm_fast_data = np.load(\"time_of_tmm_fast_wl_theta_arr_exp_80_layer.npy\")\n",
+        "vtmm_data = np.load(\"time_of_vtmm_wl_theta_arr_exp_80_layer.npy\")\n",
+        "tmmax_data = np.load(\"time_of_tmmax_wl_theta_arr_exp_80_layer.npy\")\n",
+        "\n",
+        "# Create figure and axes\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(25, 5), constrained_layout=False)\n",
+        "fig.subplots_adjust(wspace=0.35)\n",
+        "titles = ['tmm', 'tmm-fast', 'vtmm', 'tmmax']\n",
+        "\n",
+        "# Plot the first heatmap and its colorbar\n",
+        "im1 = axes[0].imshow(tmm_data, cmap='YlOrRd', aspect='equal')\n",
+        "axes[0].set_title(titles[0], fontsize=14, fontweight='bold')\n",
+        "axes[0].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[0].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[0].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[0].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[0].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[0].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[0].invert_yaxis()\n",
+        "cbar1 = fig.colorbar(im1, ax=axes[0], fraction=0.046, pad=0.04)\n",
+        "cbar1.set_label(\"Time(s)\", fontsize=10, fontweight='bold')\n",
+        "cbar1.set_ticks(np.linspace(np.min(tmm_data), np.max(tmm_data), 7, endpoint=True))\n",
+        "\n",
+        "# Plot the second heatmap and its colorbar\n",
+        "im2 = axes[1].imshow(tmm_fast_data, cmap='PuRd', aspect='equal')\n",
+        "axes[1].set_title(titles[1], fontsize=14, fontweight='bold')\n",
+        "axes[1].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[1].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[1].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[1].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[1].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[1].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[1].invert_yaxis()\n",
+        "cbar2 = fig.colorbar(im2, ax=axes[1], fraction=0.046, pad=0.04)\n",
+        "cbar2.set_label(\"Time(s)\", fontsize=10, fontweight='bold')\n",
+        "cbar2.set_ticks(np.linspace(np.min(tmm_fast_data), np.max(tmm_fast_data), 7, endpoint=True))\n",
+        "# Plot the second heatmap and its colorbar\n",
+        "im3 = axes[2].imshow(vtmm_data, cmap='PuBuGn', aspect='equal')\n",
+        "axes[2].set_title(titles[2], fontsize=14, fontweight='bold')\n",
+        "axes[2].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[2].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[2].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[2].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[2].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[2].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[2].invert_yaxis()\n",
+        "\n",
+        "# Plot the third heatmap and its colorbar\n",
+        "im4 = axes[3].imshow(tmmax_data, cmap='PuBuGn', aspect='equal')\n",
+        "axes[3].set_title(titles[3], fontsize=14, fontweight='bold')\n",
+        "axes[3].set_xlabel(\"Angle of Incidence Array Length\", fontsize=10)\n",
+        "axes[3].set_ylabel(\"Wavelength Array Length\", fontsize=10)\n",
+        "axes[3].set_xticks([0, 4, 8, 12, 16, 19])\n",
+        "axes[3].set_yticks([19, 16, 12, 8, 4, 0])\n",
+        "axes[3].set_xticklabels([\"2\",\"20\",\"40\",\"60\",\"80\",\"100\"])\n",
+        "axes[3].set_yticklabels([\"100\",\"80\",\"60\",\"40\",\"20\",\"2\"])\n",
+        "axes[3].invert_yaxis()\n",
+        "# Create a shared colorbar for the second and third heatmaps\n",
+        "# Adjust the colorbar range based on the min and max values of both the second and third imshow\n",
+        "vmin = min(im3.get_clim()[0], im4.get_clim()[0])\n",
+        "vmax = max(im3.get_clim()[1], im4.get_clim()[1])\n",
+        "im3.set_clim(vmin, vmax)\n",
+        "im4.set_clim(vmin, vmax)\n",
+        "\n",
+        "# Create a shared colorbar for the second and third heatmaps\n",
+        "cbar3 = fig.colorbar(im4, ax=axes[2:], orientation='vertical', fraction=0.02, pad=0.04)\n",
+        "cbar3.set_label(\"Time(s)\", fontsize=10, fontweight='bold')\n",
+        "cbar3.set_ticks(np.linspace(np.min(np.minimum(vtmm_data, tmmax_data)), np.max(np.maximum(vtmm_data, tmmax_data)), 7, endpoint=True))\n",
+        "plt.savefig(\"wl_theta_arr_benchmark_80_layer.svg\")\n",
+        "plt.savefig(\"wl_theta_arr_benchmark_80_layer.png\", dpi = 600)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256
+        },
+        "id": "SFZnkHuOEKKV",
+        "outputId": "6acb0bc6-44c5-4457-89ac-7723393bdf6e"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2500x500 with 7 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/benchmarks/utils.py b/benchmarks/utils.py
new file mode 100644
index 0000000..4b669e7
--- /dev/null
+++ b/benchmarks/utils.py
@@ -0,0 +1,337 @@
+import numpy as np
+import jax.numpy as jnp
+import tensorflow as tf
+
+from tmm import coh_tmm
+from tmmax.data import interpolate_nk, material_distribution_to_set, create_data
+
+from typing import List, Callable, Tuple, Union
+
+
+speed_of_light = 299792458 # m/s
+
+def get_nk_values(wl: float, nk_functions: List[Callable[[float], complex]], material_list: List[int]) -> np.ndarray:
+    """
+    This function retrieves the refractive index and extinction coefficient values 
+    (represented as complex numbers) for a given wavelength `wl` from a list of materials.
+    Each material has a corresponding function in the `nk_functions` list that, 
+    when provided with the wavelength, returns the refractive index (n) and extinction 
+    coefficient (k) as a complex number (n + j*k). The materials are indexed 
+    by `material_list` to access their corresponding functions.
+
+    Args:
+    wl (float): The wavelength at which to compute the refractive index and extinction coefficient.
+    nk_functions (List[Callable[[float], complex]]): A list of functions, each corresponding to 
+    a material's refractive index and extinction coefficient. These functions take the wavelength 
+    as input and return a complex number (n + j*k).
+    material_list (List[int]): A list of indices, where each index corresponds to a material, 
+    and is used to retrieve the respective function from the `nk_functions` list.
+
+    Returns:
+    np.ndarray: An array of complex numbers where each entry corresponds to the refractive index (n) 
+    and extinction coefficient (k) of a material at the given wavelength `wl`.
+
+    """
+    
+    return np.array([nk_functions[mat_idx](wl) for mat_idx in material_list])  # Convert the resulting list to a NumPy array.
+
+def generate_tmm_args(material_list: List[str], 
+                      thickness_list: Union[np.ndarray, float],
+                      wavelength_arr: Union[np.ndarray, float]) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    This function calculates the reflection (R) and transmission (T) for a multilayer thin film stack 
+    over arrays of wavelengths and angles of incidence using the coherent Transfer Matrix Method (TMM). 
+    The polarization of the incident light is considered either "s" (TE) or "p" (TM). The function 
+    interpolates the refractive index (n) and extinction coefficient (k) for the materials at given 
+    wavelengths and applies TMM over the material layers for each wavelength and angle of incidence.
+    
+    Args:
+        polarization (str): Polarization of the incident light. Either "s" (TE) or "p" (TM).
+        material_list (List[str]): List of material names for each layer.
+        thickness_list (Union[np.ndarray, float]): Array of thicknesses for each layer in nanometers.
+        angle_of_incidences (Union[np.ndarray, float]): Array of angles of incidence in degrees.
+        wavelength_arr (Union[np.ndarray, float]): Array of wavelengths in nanometers.
+        
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: Two 2D arrays for reflection (R) and transmission (T). 
+        The shape of these arrays will be (len(wavelength_arr), len(angle_of_incidences)).
+    """
+
+    # Create a set of unique materials to avoid redundant interpolation # The list(set(...)) ensures unique materials.
+    material_set = list(set(material_list))  
+    
+    # Assign each unique material an enumerated integer value to map it efficiently later # A dictionary with material names as keys and unique indices as values.
+    material_enum = {material: i for i, material in enumerate(material_set)}
+    
+    # Replace material names in the list with their enumerated integer index using the dictionary created # Converts material names in material_list to their corresponding integer identifiers.
+    material_list = [int(material_enum[material]) for material in material_list]
+    
+    # Create a dictionary mapping the enumerated material indices to their interpolated nk functions # Prepares interpolation functions for each unique material's refractive index data.
+    nk_funkcs = {i: interpolate_nk(material) for i, material in enumerate(material_set)}
+    
+    # Extend the thickness list by adding infinite boundaries for air above and below the stack # np.inf ensures that the first and last "layers" are considered infinitely thick (air layers).
+    thickness_list = np.concatenate(([np.inf], thickness_list, [np.inf]), axis=None)
+
+    nk_list = get_nk_values(wavelength_arr, nk_funkcs, material_list)
+
+    return thickness_list, nk_list
+
+def tmm_coh_tmm_wrapper(polarization: str, 
+                        nk_list: np.ndarray, 
+                        thickness_list: np.ndarray, 
+                        angle_of_incidences: np.ndarray, 
+                        wavelength_arr: np.ndarray ) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Args:
+        polarization (str): Polarization of the incident light. Either "s" (TE) or "p" (TM).
+        nk_list (List[str]): List of material names for each layer.
+        thickness_list (Union[np.ndarray, float]): Array of thicknesses for each layer in nanometers.
+        angle_of_incidences (Union[np.ndarray, float]): Array of angles of incidence in degrees.
+        wavelength_arr (Union[np.ndarray, float]): Array of wavelengths in nanometers.
+        
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: Two 2D arrays for reflection (R) and transmission (T). 
+        The shape of these arrays will be (len(wavelength_arr), len(angle_of_incidences)).
+    """
+    # Initialize empty arrays for storing reflection (R) and transmission (T) results # The result arrays have dimensions len(wavelength_arr) x len(angle_of_incidences).
+    R = np.zeros((len(wavelength_arr), len(angle_of_incidences)), dtype=np.float64)
+    T = np.zeros((len(wavelength_arr), len(angle_of_incidences)), dtype=np.float64)
+    
+    # Nested loops to compute R and T for each combination of wavelength and angle of incidence # Outer loop: iterating over wavelengths; Inner loop: iterating over angles of incidence.
+    for i in range(len(wavelength_arr)):
+        for j in range(len(angle_of_incidences)):
+            
+            # Retrieve the refractive index (n) and extinction coefficient (k) for each material at the current wavelength # nk_list contains n and k for all materials at wavelength_arr[i].
+            one_wl_nk_list = nk_list[:,i]
+            
+            # Perform the coherent TMM calculation using the polarization, nk_list, thicknesses, and current angle/wavelength # The result is a dictionary containing 'R' and 'T'.
+            result = coh_tmm(polarization, one_wl_nk_list, thickness_list, angle_of_incidences[j], wavelength_arr[i])
+            
+            # Store the calculated reflection (R) and transmission (T) in the result arrays # Assign reflection and transmission values to the corresponding index in R and T arrays.
+            R[i,j] = result['R']
+            T[i,j] = result['T']
+
+    # Return the final reflection (R) and transmission (T) arrays for all wavelengths and angles # These are 2D arrays, where each element corresponds to a specific wavelength and angle.
+    return R, T
+
+def generate_vtmm_args(wavelength_arr: np.ndarray, 
+                       angle_of_incidences: np.ndarray, 
+                       thickness_list: np.ndarray, 
+                       material_list: List[str]) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Generates and prepares arguments required for the vtmm.
+    
+    Arguments:
+    ----------
+    wavelength_arr : np.ndarray
+        An array of wavelengths (in meters) for which calculations will be performed.
+    angle_of_incidences : np.ndarray
+        An array of angles of incidence (in radians) corresponding to the wavelengths.
+    thickness_list : np.ndarray
+        An array representing the thicknesses of different layers in the multilayer system.
+    material_list : List[str]
+        A list of material names corresponding to each layer in the system.
+
+    Returns:
+    --------
+    omega : np.ndarray
+        Angular frequencies (omega) computed from the input wavelength array.
+    kx : np.ndarray
+        The parallel component of the wavevector computed from the angles of incidence and wavelengths.
+    nk_list : np.ndarray
+        The refractive index and extinction coefficient values for each material at the first wavelength.
+    thickness_list : np.ndarray
+        The layer thicknesses, converted to TensorFlow tensors for further computations.
+    """
+
+    global speed_of_light # Global variable representing the speed of light
+    
+    # Create a unique set of materials from the input material list to avoid duplicates
+    material_set = list(set(material_list))  # Convert material list to set for unique materials
+    
+    # Create an enumeration mapping of materials to integers for easier indexing in future calculations
+    material_enum = {material: i for i, material in enumerate(material_set)}  # Material to index dictionary
+    
+    # Replace material names in the material list with their corresponding numerical indices
+    material_list = [int(material_enum[material]) for material in material_list]  # List of material indices
+    
+    # Retrieve interpolating functions for refractive index and extinction coefficient for each material
+    nk_funkcs = {i: interpolate_nk(material) for i, material in enumerate(material_set)}  # Functions for n and k values
+    
+    # Get the refractive index and extinction coefficient values for the first wavelength in the array
+    # Using nk_funkcs, this retrieves n and k values for all materials at the specified wavelength
+    nk_list = get_nk_values(wavelength_arr[0], nk_funkcs, material_list)  # n and k values for the materials
+    nk_list = tf.cast(tf.convert_to_tensor(nk_list), tf.float64) # Convert n and k values to a TensorFlow tensor
+    
+    # Calculate angular frequency (omega) from the wavelength array. Omega = 2 * pi * c / wavelength
+    omega = speed_of_light / wavelength_arr * 2 * np.pi  # Convert wavelength to angular frequency
+    omega = tf.cast(tf.convert_to_tensor(omega), tf.float64) # Convert omega to a TensorFlow tensor
+    
+    
+    # Calculate the parallel component of the wavevector (kx) from the angle of incidence and wavelength
+    kx = np.sin(angle_of_incidences) * 2 * np.pi / wavelength_arr[0]  # Calculate kx from angles and wavelength
+    kx = tf.cast(tf.convert_to_tensor(kx), tf.float64) # Convert kx to a TensorFlow tensor
+    
+    kx = tf.constant(kx) # Ensure kx is a TensorFlow constant
+    
+    thickness_list = tf.cast(tf.convert_to_tensor(thickness_list), tf.float64) # Convert thicknesses to TensorFlow tensor
+    
+    # Return the calculated parameters required for vtmm function
+    return omega, kx, nk_list, thickness_list
+
+def generate_tmmax_args(material_list: List[str], polarization: str):
+    """
+    Generates the required arguments for tmmax function
+
+    Arguments:
+    - material_list (List[str]): A list of material names defining the structure.
+    - polarization (str): The polarization type, which must be either 's' or 'p'.
+
+    Returns:
+    - data (jnp.ndarray): A jax array containing optical properties (such as refractive index and extinction coefficient)
+      for each unique material in the input list. This data is necessary for performing tmmax calculations.
+    - material_distribution (List[str]): A processed list that maintains the original material sequence,
+      ensuring correct layer stacking in the simulation.
+    - polarization (jnp.ndarray): A JAX array of shape (1,) with a boolean value.
+      - `False` represents s-polarization.
+      - `True` represents p-polarization.
+    
+    Raises:
+    - TypeError: If the input polarization string is not 's' or 'p'.
+    """
+
+    # Convert material_list into a unique material set and maintain its distribution order.
+    material_set, material_distribution = material_distribution_to_set(material_list)
+    
+    # Create the required material data, such as refractive index and extinction coefficient, for each material in the set.
+    # This function retrieves optical properties needed for tmmax calculations.
+    data = create_data(material_set)
+
+    # Check the polarization input and convert it to a boolean JAX array.
+    if polarization == 's':
+        # For s-polarization, set the boolean flag to `False`.
+        polarization = jnp.array([False], dtype=bool)
+    elif polarization == 'p':
+        # For p-polarization, set the boolean flag to `True`.
+        polarization = jnp.array([True], dtype=bool)
+    else:
+        # Raise an error if the polarization input is invalid.
+        raise TypeError("The polarization can be 's' or 'p', not other parts. Correct it")
+
+    return data, material_distribution, polarization
+
+
+def generate_tmm_fast_args(material_list: List[str], 
+                           thickness_list: Union[np.ndarray, float],
+                           wavelength_arr: Union[np.ndarray, float]) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Generates the necessary arguments for a tmm-fast simulation in an optimized manner.
+    
+    Arguments:
+    ----------
+    material_list : List[str]
+        A list of material names (strings) corresponding to each layer in the stack.
+        
+    thickness_list : Union[np.ndarray, float]
+        An array (or a single float) representing the thickness of each material layer.
+        
+    wavelength_arr : Union[np.ndarray, float]
+        An array (or a single float) representing the wavelength values for the simulation.
+        
+    Returns:
+    --------
+    M : np.ndarray
+        A complex-valued 3D array of shape (1, num_layers, num_wavelengths) containing the refractive 
+        index values for each layer at each wavelength. 
+        
+    T : np.ndarray
+        A 2D array of shape (1, num_layers) containing the thickness values for each layer, including 
+        the added infinite air boundaries.
+    """
+
+    # Create a set of unique materials to avoid redundant interpolation
+    # The list(set(...)) ensures unique materials are stored only once.
+    material_set = list(set(material_list))  
+    
+    # Assign each unique material an enumerated integer value to map it efficiently later
+    # A dictionary with material names as keys and unique indices as values.
+    material_enum = {material: i for i, material in enumerate(material_set)}
+    
+    # Replace material names in the list with their enumerated integer index using the dictionary created
+    # Converts material names in material_list to their corresponding integer identifiers.
+    material_list = [int(material_enum[material]) for material in material_list]
+    
+    # Create a dictionary mapping the enumerated material indices to their interpolated nk functions
+    # Each function is responsible for interpolating the refractive index at a given wavelength.
+    nk_funkcs = {i: interpolate_nk(material) for i, material in enumerate(material_set)}
+    
+    # Extend the thickness list by adding infinite boundaries for air above and below the stack
+    # np.inf ensures that the first and last "layers" are considered infinitely thick (air layers).
+    thickness_list = np.concatenate(([np.inf], thickness_list, [np.inf]), axis=None)
+
+    # Compute the refractive index values for each material at the given wavelengths
+    nk_list = get_nk_values(wavelength_arr, nk_funkcs, material_list)
+
+    # Initialize an array M with shape (1, num_layers, num_wavelengths) to store refractive index values
+    # The dtype is set to np.complex128 to account for materials with non-zero extinction coefficients.
+    M = np.ones((1, len(thickness_list), len(wavelength_arr)), dtype=np.complex128)
+
+    # Assign the computed refractive index values to M
+    M[0,:,:] = nk_list
+
+    # Initialize an array T with shape (1, num_layers) to store thickness values
+    T = np.zeros((1, len(thickness_list)))
+
+    # Assign the computed thickness values (including air boundaries) to T
+    T[0,:] = thickness_list
+    
+    return M, T
+
+def generate_material_distribution_indices(N, low=0, high=10):
+    """
+    Generates an array of random integers with length N such that
+    no two consecutive elements are the same.
+
+    Parameters:
+        N (int): Length of the array.
+        low (int): Minimum value of the integers (inclusive).
+        high (int): Maximum value of the integers (exclusive).
+
+    Returns:
+        numpy.ndarray: Array of random integers with no consecutive elements the same.
+    """
+    if N <= 0:
+        raise ValueError("Array length N must be positive.")
+    
+    arr = np.zeros(N, dtype=int)
+    arr[0] = np.random.randint(low, high)
+    
+    for i in range(1, N):
+        prev = arr[i-1]
+        new_val = prev
+        while new_val == prev:
+            new_val = np.random.randint(low, high)
+        arr[i] = new_val
+
+    return arr
+
+def generate_material_list_with_air(index_array, material_list):
+    """
+    Takes an array of indices and a material list, and generates a list of materials
+    based on the index array, with 'Air' concatenated at the start and end.
+
+    Parameters:
+        index_array (numpy.ndarray): Array of integers representing indices.
+        material_list (list of str): List of material names (strings).
+
+    Returns:
+        list of str: List of materials with 'Air' at the start and end.
+    """
+    if not all(0 <= idx < len(material_list) for idx in index_array):
+        raise ValueError("Index out of bounds for the material list.")
+    
+    # Generate the list of materials
+    material_sequence = [material_list[idx] for idx in index_array]
+    
+    # Concatenate "Air" at the start and end
+    return ["Air"] + material_sequence + ["Air"]
\ No newline at end of file
diff --git a/benchmarks/vmap_array_length_benchmark.py b/benchmarks/vmap_array_length_benchmark.py
new file mode 100644
index 0000000..7b957ed
--- /dev/null
+++ b/benchmarks/vmap_array_length_benchmark.py
@@ -0,0 +1,90 @@
+import numpy as np
+import jax
+import jax.numpy as jnp
+import timeit
+
+from utils import generate_material_distribution_indices
+from utils import generate_material_list_with_air
+
+from utils import generate_tmm_args, tmm_coh_tmm_wrapper
+from utils import generate_vtmm_args
+from utils import generate_tmm_fast_args
+from utils import generate_tmmax_args
+
+from tmmax.tmm import vectorized_coh_tmm
+from vtmm import tmm_rt
+from tmm_fast import coh_tmm
+
+wl_arr_lengths = np.arange(2, 100, 5, dtype = int)
+angle_of_incidences_arr_lengths = np.arange(2, 100, 5, dtype = int)
+timeit_repetition = 50
+num_of_layers = 8 # or 80
+
+material_set = ["SiO2", "TiO2", "MgF2", "MgO", "SiO", "Al2O3", "CdS"]
+polarization = 's'
+
+indices = generate_material_distribution_indices(num_of_layers, low=0, high=len(material_set))
+material_list = generate_material_list_with_air(indices, material_set) # np.load(f"material_distribution_with_layer_num_{num_of_layers}_wl_arr_exp.npy", allow_pickle=True)
+np.save(f"material_distribution_with_layer_num_{num_of_layers}_wl_arr_exp.npy", material_list)
+
+thickness_list  = np.random.uniform(100, 500, num_of_layers)*1e-9 # np.load(f"thickness_list_with_layer_num_{num_of_layers}_wl_arr_exp.npy")
+thickness_list_tmmax = jnp.array(thickness_list)
+np.save(f"thickness_list_with_layer_num_{num_of_layers}_wl_arr_exp.npy", thickness_list)
+
+time_tmm = np.zeros((len(wl_arr_lengths), len(angle_of_incidences_arr_lengths))) 
+time_vtmm = np.zeros((len(wl_arr_lengths), len(angle_of_incidences_arr_lengths)))
+time_tmm_fast = np.zeros((len(wl_arr_lengths), len(angle_of_incidences_arr_lengths)))
+time_tmmax = np.zeros((len(wl_arr_lengths), len(angle_of_incidences_arr_lengths))) 
+
+for i in range(len(wl_arr_lengths)):
+    for j in range(len(angle_of_incidences_arr_lengths)):
+        
+        wl_arr_length = wl_arr_lengths[i]
+        angle_of_incidences_arr_length = angle_of_incidences_arr_lengths[j]
+
+        wavelength_arr = np.linspace(500e-9, 1000e-9, wl_arr_length)
+        wavelength_arr_tmmax = jnp.array(wavelength_arr)
+
+        angle_of_incidences = np.linspace(0, np.pi/2, angle_of_incidences_arr_length)
+        angle_of_incidences_tmmax = jnp.array(angle_of_incidences)
+
+        thickness_list_tmm, nk_list_tmm = generate_tmm_args(material_list = material_list, 
+                                                        thickness_list = thickness_list,
+                                                        wavelength_arr = wavelength_arr)
+        
+        omega_vtmm, kx_vtmm, nk_list_vtmm, thickness_list_vtmm = generate_vtmm_args(wavelength_arr = wavelength_arr, 
+                                                                                    angle_of_incidences = angle_of_incidences,
+                                                                                    thickness_list = thickness_list,
+                                                                                    material_list = material_list)
+        
+        t_tmm = timeit.timeit( lambda: tmm_coh_tmm_wrapper(polarization, nk_list_tmm, thickness_list_tmm, angle_of_incidences, wavelength_arr),  number=timeit_repetition )
+        t_vtmm = timeit.timeit( lambda: tmm_rt(polarization, omega_vtmm, kx_vtmm, nk_list_vtmm, thickness_list_vtmm),  number = timeit_repetition )
+
+        M_tmm_fast, T_tmm_fast = generate_tmm_fast_args(material_list = material_list, 
+                                                        thickness_list = thickness_list,
+                                                        wavelength_arr = wavelength_arr)
+
+        t_tmm_fast = timeit.timeit( lambda: coh_tmm(polarization, M_tmm_fast, T_tmm_fast, angle_of_incidences, wavelength_arr, device='cpu'),  number = timeit_repetition )
+
+
+        thickness_list_tmmax = jnp.array(thickness_list)
+        
+        data_tmmax, material_distribution_tmmax, polarization_tmmax = generate_tmmax_args(material_list = material_list, polarization = polarization)
+        
+        t_tmmax = timeit.timeit( lambda: jax.block_until_ready(vectorized_coh_tmm(data_tmmax, material_distribution_tmmax, thickness_list_tmmax, wavelength_arr_tmmax, angle_of_incidences_tmmax, polarization_tmmax)),  number = timeit_repetition)
+        
+        time_tmm[i,j] = t_tmm
+        time_vtmm[i,j] = t_vtmm
+        time_tmm_fast[i,j] = t_tmm_fast
+        time_tmmax[i,j] = t_tmmax
+
+        print(f"{wl_arr_length}, {angle_of_incidences_arr_length} tmm took ", t_tmm)
+        print(f"{wl_arr_length}, {angle_of_incidences_arr_length} vtmm took ", t_vtmm)
+        print(f"{wl_arr_length}, {angle_of_incidences_arr_length} tmm fast took ", t_tmm_fast)
+        print(f"{wl_arr_length}, {angle_of_incidences_arr_length} tmmax took ", t_tmmax)
+
+
+np.save("time_of_tmm_wl_theta_arr_exp4.npy", time_tmm)
+np.save("time_of_vtmm_wl_theta_arr_exp4.npy", time_vtmm)
+np.save("time_of_tmm_fast_wl_theta_arr_exp_8.npy", time_tmm_fast)
+np.save("time_of_tmmax_wl_theta_arr_exp4.npy", time_tmmax)
\ No newline at end of file
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diff --git a/docs/examples/example_1_visualizing_nk_data/visualizing_nk_data.ipynb b/docs/examples/example_1_visualizing_nk_data/visualizing_nk_data.ipynb
new file mode 100644
index 0000000..a598aac
--- /dev/null
+++ b/docs/examples/example_1_visualizing_nk_data/visualizing_nk_data.ipynb
@@ -0,0 +1,23 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08db69fe-d478-4695-9018-4520a79872d6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "",
+   "name": ""
+  },
+  "language_info": {
+   "name": ""
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/example_2_add_material_to_nk_database/add_material_to_nk_database.ipynb b/docs/examples/example_2_add_material_to_nk_database/add_material_to_nk_database.ipynb
new file mode 100644
index 0000000..409c4c4
--- /dev/null
+++ b/docs/examples/example_2_add_material_to_nk_database/add_material_to_nk_database.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
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+   "source": []
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diff --git a/docs/examples/example_3_two_layer_w_coating/stack_of_w_coat.png b/docs/examples/example_3_two_layer_w_coating/stack_of_w_coat.png
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diff --git a/docs/examples/example_3_two_layer_w_coating/two_layer_w_coating.ipynb b/docs/examples/example_3_two_layer_w_coating/two_layer_w_coating.ipynb
new file mode 100644
index 0000000..86d8d15
--- /dev/null
+++ b/docs/examples/example_3_two_layer_w_coating/two_layer_w_coating.ipynb
@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b504b0a4-8d1d-4fdc-b5d3-5b1aa76159f8",
+   "metadata": {
+    "id": "b504b0a4-8d1d-4fdc-b5d3-5b1aa76159f8"
+   },
+   "source": [
+    "# **Reflectance Performance of the W-coat on a Glass Substrate**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9089a62d-4ee0-4bd7-ab7b-3d867e55439b",
+   "metadata": {
+    "id": "9089a62d-4ee0-4bd7-ab7b-3d867e55439b"
+   },
+   "source": [
+    "This notebook demonstrates how to calculate the reflectance of a multilayer thin film stack (W-coat) deposited on a glass substrate. The calculation is performed for two types of polarizations, s-polarization and p-polarization, over a wavelength range of 500 nm to 700 nm and incident angles from 0° to 70°.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a193c6b-2c46-4002-8659-3fe1c96b1b63",
+   "metadata": {
+    "id": "6a193c6b-2c46-4002-8659-3fe1c96b1b63"
+   },
+   "source": [
+    "## **Introduction**\n",
+    "\n",
+    "We will use the Transfer Matrix Method (TMM) to analyze the reflection behavior of this multilayer thin film. The W-coat consists of two layers made from Magnesium Fluoride (MgF₂) and Yttrium Oxide (Y₂O₃). These layers are stacked on a glass substrate (Silicon Dioxide, SiO₂), and the incident medium is air.\n",
+    "\n",
+    "![title](stack_of_w_coat.png)\n",
+    "\n",
+    "In this example, the calculation is done for two polarizations:\n",
+    "- **s-polarization**: Electric field perpendicular to the plane of incidence.\n",
+    "- **p-polarization**: Electric field parallel to the plane of incidence.\n",
+    "\n",
+    "We will calculate the reflectance as a function of wavelength and angle of incidence for each polarization.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36dd9f03-d456-46a2-9236-35d826f3bcc1",
+   "metadata": {
+    "id": "36dd9f03-d456-46a2-9236-35d826f3bcc1"
+   },
+   "source": [
+    "## **Materials and Layer Thickness**\n",
+    "\n",
+    "We are considering the following material stack:\n",
+    "\n",
+    "- **Air** as the incident medium.\n",
+    "- **MgF₂ (Magnesium Fluoride)** with a thickness of 93 nm.\n",
+    "- **Y₂O₃ (Yttrium Oxide)** with a thickness of 63 nm.\n",
+    "- **SiO₂ (Silicon Dioxide)** as the substrate.\n",
+    "\n",
+    "These materials are well-suited for anti-reflection coatings and optical filters due to their refractive indices.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dedec82-aefa-4bc7-bd78-3fc1c65a3c52",
+   "metadata": {
+    "id": "2dedec82-aefa-4bc7-bd78-3fc1c65a3c52"
+   },
+   "source": [
+    "## **Python Implementation**\n",
+    "\n",
+    "In this section, we implement the TMM algorithm using the `tmmax` library, which is designed for high-performance multilayer simulations with vectorized operations using the `jax` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "415aebc4-8673-4c62-ba6f-730b4b411bb7",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "415aebc4-8673-4c62-ba6f-730b4b411bb7",
+    "outputId": "a865957b-dbc8-43f1-f568-aabff48ad3ae"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/jax/_src/ops/scatter.py:96: FutureWarning: scatter inputs have incompatible types: cannot safely cast value from dtype=complex128 to dtype=float32 with jax_numpy_dtype_promotion='standard'. In future JAX releases this will result in an error.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.10/dist-packages/jax/_src/ops/scatter.py:136: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return lax_internal._convert_element_type(out, dtype, weak_type)\n",
+      "/usr/local/lib/python3.10/dist-packages/jax/_src/ops/scatter.py:96: FutureWarning: scatter inputs have incompatible types: cannot safely cast value from dtype=complex128 to dtype=float32 with jax_numpy_dtype_promotion='standard'. In future JAX releases this will result in an error.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.10/dist-packages/jax/_src/ops/scatter.py:136: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return lax_internal._convert_element_type(out, dtype, weak_type)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import jax.numpy as jnp\n",
+    "from tmmax.tmm import tmm\n",
+    "\n",
+    "# Define the materials and their thicknesses\n",
+    "material_list =  [\"Air\", \"MgF2\", \"Y2O3\", \"SiO2\"]\n",
+    "thickness_list = jnp.array([93e-9, 63e-9])\n",
+    "\n",
+    "# Define the wavelength range (500 nm to 700 nm) and angles of incidence (0° to 70°)\n",
+    "wavelength_arr = jnp.linspace(500e-9, 700e-9, 1000)\n",
+    "angle_of_incidences = jnp.linspace(0, (70*jnp.pi/180), 1000)\n",
+    "\n",
+    "# Calculate reflectance for s-polarization\n",
+    "result_s = tmm(material_list=material_list, thickness_list=thickness_list,\n",
+    "             wavelength_arr=wavelength_arr, angle_of_incidences=angle_of_incidences,\n",
+    "             polarization=\"s\")\n",
+    "\n",
+    "# Calculate reflectance for p-polarization\n",
+    "result_p = tmm(material_list=material_list, thickness_list=thickness_list,\n",
+    "             wavelength_arr=wavelength_arr, angle_of_incidences=angle_of_incidences,\n",
+    "             polarization=\"p\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1d79de7-55f0-416a-b254-0dacc1bc8505",
+   "metadata": {
+    "id": "f1d79de7-55f0-416a-b254-0dacc1bc8505"
+   },
+   "source": [
+    "## **Visualization of Results**\n",
+    "\n",
+    "We will now visualize the reflectance for both s-polarization and p-polarization as a function of wavelength and angle of incidence. The color map will represent the reflectance percentage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a760a4be-70a6-49a1-9d7a-950c8becb201",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "a760a4be-70a6-49a1-9d7a-950c8becb201",
+    "outputId": "16724c8c-2bce-48ad-ed14-9ea0399b4239"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
+    "\n",
+    "# Set up figure and image grid\n",
+    "fig = plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "grid = ImageGrid(fig, 111,\n",
+    "                 nrows_ncols=(1,2),\n",
+    "                 axes_pad=0.65,\n",
+    "                 share_all=True,\n",
+    "                 cbar_location=\"right\",\n",
+    "                 cbar_mode=\"single\",\n",
+    "                 cbar_size=\"5%\",\n",
+    "                 cbar_pad=0.25,\n",
+    "                 )\n",
+    "\n",
+    "# Add data to image grid\n",
+    "i = 0\n",
+    "for ax in grid:\n",
+    "    if i == 0:\n",
+    "        im = ax.imshow(result_s[0]*100, cmap='Spectral', aspect=200/70,extent = [500, 700, 0, 70])\n",
+    "        ax.set_title(\"s-polarization\")\n",
+    "    if i ==1:\n",
+    "        im = ax.imshow(result_p[0]*100, cmap='Spectral', aspect=200/70,extent = [500, 700, 0, 70])\n",
+    "        ax.set_title(\"p-polarization\")\n",
+    "    ax.set_xlabel(\"Wavelength (nm)\")\n",
+    "    ax.set_ylabel(\"Incident angle ($^o$)\")\n",
+    "    i += 1\n",
+    "\n",
+    "# Colorbar\n",
+    "ax.cax.colorbar(im, label='Reflectance (%)')\n",
+    "ax.cax.toggle_label(True)\n",
+    "\n",
+    "plt.savefig(\"w_coat_reflectance_s_p.png\", dpi=600)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e777fe08-bee7-445c-9214-1a4bace8f1c6",
+   "metadata": {
+    "id": "e777fe08-bee7-445c-9214-1a4bace8f1c6"
+   },
+   "source": [
+    "### **Discussion of Reflectance Results**\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### **s-Polarization**\n",
+    "The first heatmap (on the left) represents the reflectance of the thin-film stack for **s-polarized light** (electric field perpendicular to the plane of incidence). The key observations from this plot include:\n",
+    "\n",
+    "- **Reflectance Trends**:\n",
+    "  - At lower wavelengths (~500 nm), the reflectance is higher for near-normal incidence (small angles) and gradually decreases as the angle of incidence increases. This is visually apparent by the transition from a light green at the lower left corner to darker colors at higher angles.\n",
+    "  - For wavelengths above ~550 nm, the reflectance remains relatively low across all angles of incidence. This is indicated by the uniformity in the darker red and purple regions of the heatmap.\n",
+    "  \n",
+    "- **Angle Dependence**:\n",
+    "  - As the angle increases, the reflectance generally rises for wavelengths below ~600 nm. This behavior can be seen in the color gradient from the blue/green regions at small angles to the red/maroon shades at higher angles.\n",
+    "  - The most significant variation occurs for wavelengths in the range of 500-525 nm, where a sharp color transition occurs, indicating higher reflectance at lower wavelengths.\n",
+    "\n",
+    "#### **p-Polarization**\n",
+    "The second heatmap (on the right) shows the reflectance for **p-polarized light** (electric field parallel to the plane of incidence). The trends for p-polarization differ from those of s-polarization in several ways:\n",
+    "\n",
+    "- **Reflectance Trends**:\n",
+    "  - Similar to s-polarization, the reflectance is higher at shorter wavelengths and for near-normal incidence. However, p-polarization shows a slightly higher overall reflectance in the shorter wavelength region (500-525 nm), as seen in the more pronounced blue and green regions.\n",
+    "  - Above ~600 nm, the reflectance remains consistently low across the entire angle range, much like in s-polarization, which is reflected in the red-to-maroon shading.\n",
+    "\n",
+    "- **Angle Dependence**:\n",
+    "  - For p-polarized light, the reflectance behavior at small angles and shorter wavelengths (500-525 nm) shows a slightly steeper rise compared to s-polarization. The blue-to-green transition occurs at smaller angles in this case, indicating a more pronounced increase in reflectance at these angles.\n",
+    "  - As the angle approaches 70°, the reflectance tends to stabilize and reduce, particularly for longer wavelengths.\n",
+    "\n",
+    "#### **Comparison of s- and p-Polarization Reflectance**\n",
+    "- At **shorter wavelengths (500-525 nm)**, both s- and p-polarization reflectance exhibit higher values at normal incidence and increase with the angle of incidence. However, p-polarization shows a steeper increase compared to s-polarization at these wavelengths.\n",
+    "- For **wavelengths above ~600 nm**, the reflectance remains consistently low for both polarizations across all angles of incidence, though the p-polarization shows marginally higher values at lower angles.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3878732b-124a-4b03-8d3d-8df5703af4a9",
+   "metadata": {
+    "id": "3878732b-124a-4b03-8d3d-8df5703af4a9"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/example_4_50_50_beam_splitter/50_50_beam_splitter.ipynb b/docs/examples/example_4_50_50_beam_splitter/50_50_beam_splitter.ipynb
new file mode 100644
index 0000000..eb0716e
--- /dev/null
+++ b/docs/examples/example_4_50_50_beam_splitter/50_50_beam_splitter.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff6ab01-d66b-49b9-ae69-181e820227e4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/example_5_edge_filter/edge_filter.ipynb b/docs/examples/example_5_edge_filter/edge_filter.ipynb
new file mode 100644
index 0000000..eb0716e
--- /dev/null
+++ b/docs/examples/example_5_edge_filter/edge_filter.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff6ab01-d66b-49b9-ae69-181e820227e4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/example_6_bandpass_filter/bandpass_filter.ipynb b/docs/examples/example_6_bandpass_filter/bandpass_filter.ipynb
new file mode 100644
index 0000000..eb0716e
--- /dev/null
+++ b/docs/examples/example_6_bandpass_filter/bandpass_filter.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff6ab01-d66b-49b9-ae69-181e820227e4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/logo/logo_tmmax.png b/docs/images/logo_tmmax.png
similarity index 100%
rename from docs/logo/logo_tmmax.png
rename to docs/images/logo_tmmax.png
diff --git a/__init__.py b/requirements.txt
similarity index 100%
rename from __init__.py
rename to requirements.txt
diff --git a/tmmax/VERSION b/tmmax/VERSION
new file mode 100644
index 0000000..3eefcb9
--- /dev/null
+++ b/tmmax/VERSION
@@ -0,0 +1 @@
+1.0.0
diff --git a/tmmax/__init__.py b/tmmax/__init__.py
new file mode 100644
index 0000000..fcb2058
--- /dev/null
+++ b/tmmax/__init__.py
@@ -0,0 +1,2 @@
+from os import path
+nk_data_dir = path.join(path.dirname(__file__), 'nk_data')
\ No newline at end of file
diff --git a/tmmax/__main__.py b/tmmax/__main__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tmmax/angle.py b/tmmax/angle.py
new file mode 100644
index 0000000..598a12b
--- /dev/null
+++ b/tmmax/angle.py
@@ -0,0 +1,491 @@
+import jax.numpy as jnp  # Import the jax numpy module for numerical and mathematical operations, used for efficient array manipulation and computations on CPUs/GPUs/TPUs.
+from jax.lax import cond  # Import the 'cond' function from jax.lax, which is used to implement conditional branching in a JAX-compatible way (similar to if-else).
+from jax import Array  # Import the Array class from jax, which is used for creating arrays in JAX, though it's included here primarily for type specification purpose.
+from jax.typing import ArrayLike  # Import ArrayLike from jax.typing. It is an annotation for any value that is safe to implicitly cast to a JAX array
+import sys  # Import the sys module to access system-specific parameters, in this case, to retrieve the smallest representable positive number (epsilon).
+
+# Define EPSILON as the smallest representable positive number such that 1.0 + EPSILON != 1.0
+EPSILON = sys.float_info.epsilon  # Assign the machine epsilon value from the sys module
+
+def is_forward_if_bigger_than_eps_s_pol(n: ArrayLike, theta: ArrayLike) -> Array:
+    """ 
+    This function determines the propagation direction (forward or backward) for s-polarized light
+    based on the refractive index and angle of incidence. It uses the complex refractive index `n` 
+    (which is in the form of `n + i*k`, where `n` is the real part and `k` is the imaginary part of
+    refractive index and k is representing the extinction coefficient) and the angle `theta` (in 
+    radians) that the light makes with the normal to the interface.
+
+    The function computes `n * cos(theta)` to evaluate the propagation direction for s-polarization.
+    If the imaginary part of `n * cos(theta)` is positive, it indicates forward propagation (because 
+    of the decay of the wave in the medium). If the imaginary part is negative, it indicates backward 
+    propagation (since the wave is evanescent or decaying in the direction opposite to propagation). 
+
+    Arguments:
+    n : ArrayLike
+        This is a complex jax array representing the refractive index of a material in the multilayer stack.
+    
+    theta : ArrayLike
+        This is a jax array of angle in radians, representing the angle of incidence of the light ray 
+        with respect to the normal of the layer surface. The angle is typically in the range [0, pi/2].
+    
+    Returns:
+    Array
+        This function returns a jax array of boolean value, where represents whether the propagation for 
+        the corresponding angle and refractive index is forward (`True`) or backward (`False`).
+    """
+    
+    # Calculate n * cos(theta) to evaluate propagation direction for s-polarization
+    n_cos_theta = jnp.multiply(n, jnp.cos(theta))  # Multiply refractive index by cosine of angle
+    
+    # For evanescent or lossy mediums, forward is determined by decay
+    # Determine forward propagation by checking the sign of the imaginary part of n * cos(theta)
+    is_forward_s = jnp.invert(jnp.signbit(jnp.imag(n_cos_theta)))  # Invert the sign of the imaginary part
+    
+    # Return a boolean array where True means forward propagation, False means backward
+    return is_forward_s
+
+def is_forward_if_smaller_than_eps_s_pol(n: ArrayLike, theta: ArrayLike) -> Array:
+    """ 
+    This function determines the propagation direction (forward or backward) for s-polarized light
+    based on the refractive index and angle of incidence. It uses the complex refractive index `n` 
+    (which is in the form of `n + i*k`, where `n` is the real part and `k` is the imaginary part of
+    refractive index and k is representing the extinction coefficient) and the angle `theta` (in 
+    radians) that the light makes with the normal to the interface.
+
+    The function computes `n * cos(theta)` to evaluate the propagation direction for s-polarization.
+    If the real part of `n * cos(theta)` is positive, it indicates forward propagation (because 
+    of the decay of the wave in the medium). If the real part is negative, it indicates backward 
+    propagation (since the wave is evanescent or decaying in the direction opposite to propagation). 
+
+    Arguments:
+    n : ArrayLike
+        This is a complex jax array representing the refractive index of a material in the multilayer stack.
+    
+    theta : ArrayLike
+        This is a jax array of angle in radians, representing the angle of incidence of the light ray 
+        with respect to the normal of the layer surface. The angle is typically in the range [0, pi/2].
+    
+    Returns:
+    Array
+        This function returns a jax array of boolean value, where represents whether the propagation for 
+        the corresponding angle and refractive index is forward (`True`) or backward (`False`).
+    """
+
+    # Calculate n * cos(theta) to evaluate propagation direction for s-polarization
+    n_cos_theta = jnp.multiply(n, jnp.cos(theta))
+
+    # Check if the real part of n * cos(theta) is positive or negative to determine if it's forward or backward
+    is_forward_s = jnp.invert(jnp.signbit(jnp.real(n_cos_theta))) # Invert the sign of the real part
+
+    # Return a boolean array where True means forward propagation, False means backward
+    return is_forward_s
+
+def is_forward_if_bigger_than_eps_p_pol(n: ArrayLike, theta: ArrayLike) -> Array:
+    """ 
+    This function determines the propagation direction (forward or backward) for p-polarized light
+    based on the refractive index and angle of incidence. It uses the complex refractive index `n` 
+    (which is in the form of `n + i*k`, where `n` is the real part and `k` is the imaginary part of
+    refractive index and k is representing the extinction coefficient) and the angle `theta` (in 
+    radians) that the light makes with the normal to the interface.
+
+    The function computes `n * cos(theta)` to evaluate the propagation direction for p-polarization.
+    If the imaginary part of `n * cos(theta)` is positive, it indicates forward propagation (because 
+    of the decay of the wave in the medium). If the imaginary part is negative, it indicates backward 
+    propagation (since the wave is evanescent or decaying in the direction opposite to propagation). 
+
+    Arguments:
+    n : ArrayLike
+        This is a complex jax array representing the refractive index of a material in the multilayer stack.
+    
+    theta : ArrayLike
+        This is a jax array of angle in radians, representing the angle of incidence of the light ray 
+        with respect to the normal of the layer surface. The angle is typically in the range [0, pi/2].
+    
+    Returns:
+    Array
+        This function returns a jax array of boolean value, where represents whether the propagation for 
+        the corresponding angle and refractive index is forward (`True`) or backward (`False`).
+    """
+
+    # Calculate n * cos(theta) to evaluate propagation direction for s-polarization
+    n_cos_theta = jnp.multiply(n, jnp.cos(theta)) # Multiply refractive index by cosine of angle
+
+    # For evanescent or lossy mediums, forward is determined by decay
+    # Determine forward propagation by checking the sign of the imaginary part of n * cos(theta)
+    is_forward_p = jnp.invert(jnp.signbit(jnp.imag(n_cos_theta))) # Invert the sign of the imaginary part
+
+    # Return a boolean array where True means forward propagation, False means backward
+    return is_forward_p
+
+def is_forward_if_smaller_than_eps_p_pol(n: ArrayLike, theta: ArrayLike) -> Array:
+    """ 
+    This function determines the propagation direction (forward or backward) for p-polarized light
+    based on the refractive index and angle of incidence. It uses the complex refractive index `n` 
+    (which is in the form of `n + i*k`, where `n` is the real part and `k` is the imaginary part of
+    refractive index and k is representing the extinction coefficient) and the angle `theta` (in 
+    radians) that the light makes with the normal to the interface.
+
+    The function computes `n * cos(theta)` to evaluate the propagation direction for p-polarization.
+    If the real part of `n * cos(theta)` is positive, it indicates forward propagation (because 
+    of the decay of the wave in the medium). If the real part is negative, it indicates backward 
+    propagation (since the wave is evanescent or decaying in the direction opposite to propagation). 
+
+    Arguments:
+    n : ArrayLike
+        This is a complex jax array representing the refractive index of a material in the multilayer stack.
+    
+    theta : ArrayLike
+        This is a jax array of angle in radians, representing the angle of incidence of the light ray 
+        with respect to the normal of the layer surface. The angle is typically in the range [0, pi/2].
+    
+    Returns:
+    Array
+        This function returns a jax array of boolean value, where represents whether the propagation for 
+        the corresponding angle and refractive index is forward (`True`) or backward (`False`).
+    """
+
+    # Calculate n * cos(theta) to evaluate propagation direction for p-polarization
+    n_cos_theta_star = jnp.multiply(n, jnp.cos(jnp.conj(theta)))
+
+    # Check if the real part of n * cos(theta) is positive or negative to determine if it's forward or backward
+    is_forward_p = jnp.invert(jnp.signbit(jnp.real(n_cos_theta_star))) # Invert the sign of the real part
+
+    # Return a boolean array where True means forward propagation, False means backward
+    return is_forward_p
+
+def is_propagating_wave_s_pol(n: ArrayLike, theta: ArrayLike) -> Array:
+    """
+    This function determines whether light propagating in a layer of a multilayer thin film structure 
+    is traveling forward or backward for s-polarized light. The decision is made based on the imaginary 
+    part of the product of the refractive index (`n`) and the cosine of the angle of incidence (`theta`). 
+    If the absolute value of the imaginary component exceeds a threshold (1000 * EPSILON), 
+    the function applies one of two conditions to decide the propagation direction. 
+    
+    Arguments:
+    ----------
+    n: ArrayLike
+        The refractive index (complex) of the medium in the layer under consideration. This could have 
+        a real part (indicating normal propagation) and/or an imaginary part (indicating losses or evanescent waves).
+    
+    theta: ArrayLike
+        The angle of incidence (in radians) of the light wave within the layer. This is used to compute 
+        the cosine term for further evaluation.
+    
+    Returns:
+    --------
+    Array
+        A boolean value or array of boolean values (`True` or `False`) indicating whether the light wave 
+        is propagating forward (`True`) or backward (`False`) for s-polarization.
+    
+    Functionality:
+    --------------
+    The function first evaluates whether the product of `n * cos(theta)` has a sufficiently large imaginary part.
+    Based on this condition, it invokes one of two helper functions (`is_forward_if_bigger_than_eps_s_pol` or 
+    `is_forward_if_smaller_than_eps_s_pol`) using `jax.lax.cond` for efficient branching. The output is determined 
+    by the specific conditions defined in these helper functions.
+    """
+
+    # Calculate the condition by checking the absolute value of the imaginary part of `n * cos(theta)`.
+    # If the absolute value exceeds `1000 * EPSILON`, it is assumed that the wave is evanescent or experiencing loss.
+    condition = jnp.squeeze(jnp.greater(jnp.abs(jnp.imag(jnp.multiply(n, jnp.cos(theta)))), jnp.multiply(jnp.array([EPSILON]), jnp.array([1e3]))))
+    
+    # Use the `jax.lax.cond` function to efficiently choose between two helper functions.
+    # If the condition is True, call `is_forward_if_bigger_than_eps_s_pol` with `n` and `theta`.
+    # Otherwise, call `is_forward_if_smaller_than_eps_s_pol` with `n` and `theta`.
+    is_forward_s = cond(condition, is_forward_if_bigger_than_eps_s_pol, is_forward_if_smaller_than_eps_s_pol, n, theta)
+    
+    # Return the boolean value indicating whether the wave is forward-propagating for s-polarization.
+    return is_forward_s
+
+def is_propagating_wave_p_pol(n: ArrayLike, theta: ArrayLike) -> Array:
+    """
+    This function determines whether light propagating in a layer of a multilayer thin film structure 
+    is traveling forward or backward for p-polarized light. The decision is made based on the imaginary 
+    part of the product of the refractive index (`n`) and the cosine of the angle of incidence (`theta`). 
+    If the absolute value of the imaginary component exceeds a threshold (1000 * EPSILON), 
+    the function applies one of two conditions to decide the propagation direction. 
+    
+    Arguments:
+    ----------
+    n: ArrayLike
+        The refractive index (complex) of the medium in the layer under consideration. This could have 
+        a real part (indicating normal propagation) and/or an imaginary part (indicating losses or evanescent waves).
+    
+    theta: ArrayLike
+        The angle of incidence (in radians) of the light wave within the layer. This is used to compute 
+        the cosine term for further evaluation.
+    
+    Returns:
+    --------
+    Array
+        A boolean value or array of boolean values (`True` or `False`) indicating whether the light wave 
+        is propagating forward (`True`) or backward (`False`) for p-polarization.
+    
+    Functionality:
+    --------------
+    The function first evaluates whether the product of `n * cos(theta)` has a sufficiently large imaginary part.
+    Based on this condition, it invokes one of two helper functions (`is_forward_if_bigger_than_eps_p_pol` or 
+    `is_forward_if_smaller_than_eps_p_pol`) using `jax.lax.cond` for efficient branching. The output is determined 
+    by the specific conditions defined in these helper functions.
+    """
+
+    # Calculate the condition by checking the absolute value of the imaginary part of `n * cos(theta)`.
+    # If the absolute value exceeds `1000 * EPSILON`, it is assumed that the wave is evanescent or experiencing loss.
+    condition = jnp.squeeze(jnp.greater(jnp.abs(jnp.imag(jnp.multiply(n, jnp.cos(theta)))), jnp.multiply(jnp.array([EPSILON]), jnp.array([1e3]))))
+    
+    # Use the `jax.lax.cond` function to efficiently choose between two helper functions.
+    # If the condition is True, call `is_forward_if_bigger_than_eps_p_pol` with `n` and `theta`.
+    # Otherwise, call `is_forward_if_smaller_than_eps_p_pol` with `n` and `theta`.
+    is_forward_p = cond(condition, is_forward_if_bigger_than_eps_p_pol, is_forward_if_smaller_than_eps_p_pol, n, theta)
+    
+    # Return the boolean value indicating whether the wave is forward-propagating for p-polarization.
+    return is_forward_p
+
+def update_theta_arr_incoming(theta_array: ArrayLike) -> Array:
+    """
+    This function adjusts the angle of incidence for a multilayer thin-film system by ensuring that 
+    the light in the incoming medium propagates in a forward direction. It operates on a vector 
+    (`theta_array`) containing the angles that light makes with the normal in each layer of the multilayer 
+    structure. The theta_array values depend on the incident angle and the wavelength-dependent refractive 
+    index (`n(wavelength)`).
+
+    Specifically, if the wave in the incoming medium (the first layer) is not forward propagating, 
+    the function modifies the first element of the theta_array vector to its complement with respect to 180 degrees 
+    (pi in radians). This ensures that the light is in an optically correct format, aligning with the 
+    physical constraints of multilayer systems where light propagation must conform to specific conditions 
+    such as forward propagation in the infinitely thick incoming medium.
+
+    Arguments:
+    ----------
+    theta_array : ArrayLike
+        A vector containing the angles (in radians) that light makes with the normal in each layer 
+        of a multilayer thin-film structure. The first element corresponds to the incoming medium.
+
+    Returns:
+    --------
+    Array
+        The updated `theta_array` with the first angle corrected (if necessary) to ensure forward 
+        propagation in the incoming medium.
+    """
+
+    # Access the first element of theta_array and subtract it from pi (180 degrees in radians).
+    # This operation ensures the first angle is adjusted to its complement with respect to pi
+    # if it is not forward propagating.
+    return theta_array.at[0].set(jnp.pi - theta_array.at[0].get())
+
+def update_theta_arr_outgoing(theta_array: ArrayLike) -> Array:
+    """
+    This function updates the last element of the angle vector (`theta_array`) to ensure
+    the outgoing wave in a multilayer thin-film structure adheres to optical conventions.
+
+    In multilayer thin films, the angles in `theta_array` represent the angles of light with respect
+    to the normal in each layer. These angles are influenced by the angle of incidence and the
+    wavelength because the refractive index is wavelength-dependent (n(wavelength)).
+
+    The outgoing medium is assumed to have a forward-propagating wave because multilayer thin films
+    with complex refractive indices (n + ik) typically don't support negative refractive indices.
+    To ensure this optical consistency, the last angle in the `theta_array` (which represents the
+    outgoing medium) is updated to its complementary angle. Specifically, the last element is set 
+    to `pi - theta_array[-1]`, which effectively adjusts the angle for optical correctness.
+
+    This adjustment is critical for ensuring the outgoing light does not deviate excessively 
+    from the normal after passing through a multilayer structure, maintaining a physically and 
+    optically consistent representation of the outgoing wave.
+
+    Args:
+        theta_array (ArrayLike): A vector containing the angles of light with respect to the 
+        normal in each layer of the multilayer thin-film structure.
+
+    Returns:
+        Array: The updated angle vector (`theta_array`) with its last element adjusted to ensure
+        optical consistency for the outgoing wave.
+    """
+
+    # Access the last element of theta_array and subtract it from pi (180 degrees in radians).
+    # This operation ensures the last angle is adjusted to its complement with respect to pi
+    # if it is not forward propagating.
+    return theta_array.at[-1].set(jnp.pi - theta_array.at[-1].get())
+
+def return_unchanged_theta(theta_array: ArrayLike) -> Array:
+    """
+    This function is a placeholder or pseudo-function designed to return the input array `theta_array` without any modifications. 
+    It is primarily used when working with JAX's `lax.cond` function, where different branches (functions) need to be specified 
+    for conditional execution. 
+
+    In contexts where we do not want to alter the `theta_array` during conditional branching, this function serves as an 
+    identity function. For example, if light forward propagates in certain scenarios where the outgoing and incoming functions 
+    of `theta_array` do not require modification, this function ensures `theta_array` remains unchanged.
+
+    Arguments:
+    -----------
+    theta_array: ArrayLike
+        The input array representing `theta`, which might correspond to angular values or other parameter arrays in a 
+        computation. The function treats this array as immutable within its context.
+
+    Returns:
+    --------
+    Array
+        The same input array, `theta_array`, returned unchanged.
+    """
+    return theta_array
+
+def compute_layer_angles_s_pol(angle_of_incidence: ArrayLike,
+                               nk_list: ArrayLike) -> Array:
+    """
+    This function calculates the angles that light makes with the layer normal in each layer of a multilayer stack 
+    structure under s-polarized light. The function uses Snell's law to compute the angles for each layer, ensuring 
+    compatibility with evanescent and lossy cases by handling complex refractive indices. It validates the forward 
+    propagation conditions for the incoming and outgoing media and updates the corresponding angles in the theta array
+    if necessary. The resulting array helps determine the behavior of light within the multilayer system. 
+
+    Arguments:
+    - `angle_of_incidence` (ArrayLike): This is the angle at which light is incident on the first layer of the multilayer 
+      stack. It is expected to be in radians.
+    - `nk_list` (ArrayLike): A JAX array containing the complex refractive indices of the multilayer system, including 
+      the incoming medium, each layer in the stack, and the outgoing medium. 
+      - For a multilayer system with N layers, this array has a length of N+2: 
+        [n_incoming, n_layer_0, n_layer_1, ..., n_layer_N, n_outgoing].
+      - The complex refractive indices account for both the real part (refraction) and the imaginary part 
+        (absorption/losses).
+
+    Returns:
+    - A JAX array (`theta_array`) of the same length as `nk_list`, where each element represents the calculated angle 
+      (in radians) for each layer of the multilayer structure, including the incoming and outgoing media.
+    """
+
+    # Calculate the sine of the angles by using the first layer n * sin(theta)
+    # sin(theta_i) = (n_0 * sin(theta_in)) / n_i, where n_0 is the refractive index of the first layer
+    sin_theta = jnp.true_divide(jnp.multiply(jnp.sin(angle_of_incidence), nk_list.at[0].get()), nk_list)
+    
+    # Compute the angle (theta) in each layer using the arcsin function
+    # jnp.arcsin is used here as it supports complex values if necessary, allowing for accurate calculations 
+    # in lossy or evanescent cases
+    theta_array = jnp.arcsin(sin_theta)
+
+    # Check if the wave in the incoming medium is propagating
+    # This determines whether the computed angle is forward-facing or not
+    incoming_props = is_propagating_wave_s_pol(nk_list.at[0].get(), theta_array.at[0].get())
+
+    # Check if the wave in the outgoing medium is propagating
+    # This validates the forward propagation condition for the outgoing medium
+    outgoing_props = is_propagating_wave_s_pol(nk_list.at[-1].get(), theta_array.at[-1].get())
+
+    # Create conditions to check if the incoming wave is propagating
+    # The conditions ensure that the calculated angles are correctly oriented
+    condition_incoming = jnp.array_equal(incoming_props, jnp.array([True], dtype=bool))
+    condition_outgoing = jnp.array_equal(outgoing_props, jnp.array([True], dtype=bool))
+
+    # Update the angle for the incoming medium using JAX's conditional function (jax.lax.cond)
+    # If the condition is true, the theta array is modified for the incoming medium
+    theta_array = cond(condition_incoming, update_theta_arr_incoming, return_unchanged_theta, operand=theta_array)
+    
+    # Update the angle for the outgoing medium using JAX's conditional function (jax.lax.cond)
+    # If the condition is true, the theta array is modified for the outgoing medium
+    theta_array = cond(condition_outgoing, update_theta_arr_outgoing, return_unchanged_theta, operand=theta_array)
+    #print("third theta : ", jnp.asarray(theta_array))
+
+    # Return a 1D theta array for each layer
+    # This array contains the calculated angles for all layers
+    return theta_array
+
+def compute_layer_angles_p_pol(angle_of_incidence: ArrayLike,
+                               nk_list: ArrayLike) -> Array:
+    """
+    This function calculates the angles that light makes with the layer normal in each layer of a multilayer stack 
+    structure under p-polarized light. The function uses Snell's law to compute the angles for each layer, ensuring 
+    compatibility with evanescent and lossy cases by handling complex refractive indices. It validates the forward 
+    propagation conditions for the incoming and outgoing media and updates the corresponding angles in the theta array
+    if necessary. The resulting array helps determine the behavior of light within the multilayer system. 
+
+    Arguments:
+    - `angle_of_incidence` (ArrayLike): This is the angle at which light is incident on the first layer of the multilayer 
+      stack. It is expected to be in radians.
+    - `nk_list` (ArrayLike): A JAX array containing the complex refractive indices of the multilayer system, including 
+      the incoming medium, each layer in the stack, and the outgoing medium. 
+      - For a multilayer system with N layers, this array has a length of N+2: 
+        [n_incoming, n_layer_0, n_layer_1, ..., n_layer_N, n_outgoing].
+      - The complex refractive indices account for both the real part (refraction) and the imaginary part 
+        (absorption/losses).
+
+    Returns:
+    - A JAX array (`theta_array`) of the same length as `nk_list`, where each element represents the calculated angle 
+      (in radians) for each layer of the multilayer structure, including the incoming and outgoing media.
+    """
+
+    # Calculate the sine of the angles by using the first layer n * sin(theta)
+    # sin(theta_i) = (n_0 * sin(theta_in)) / n_i, where n_0 is the refractive index of the first layer
+    sin_theta = jnp.true_divide(jnp.multiply(jnp.sin(angle_of_incidence), nk_list.at[0].get()), nk_list)
+    
+    # Compute the angle (theta) in each layer using the arcsin function
+    # jnp.arcsin is used here as it supports complex values if necessary, allowing for accurate calculations 
+    # in lossy or evanescent cases
+    theta_array = jnp.arcsin(sin_theta)
+
+    # Check if the wave in the incoming medium is propagating
+    # This determines whether the computed angle is forward-facing or not
+    incoming_props = is_propagating_wave_p_pol(nk_list.at[0].get(), theta_array.at[0].get())
+
+    # Check if the wave in the outgoing medium is propagating
+    # This validates the forward propagation condition for the outgoing medium
+    outgoing_props = is_propagating_wave_p_pol(nk_list.at[-1].get(), theta_array.at[-1].get())
+
+    # Create conditions to check if the incoming wave is propagating
+    # The conditions ensure that the calculated angles are correctly oriented
+    condition_incoming = jnp.array_equal(incoming_props, jnp.array([True], dtype=bool))
+    condition_outgoing = jnp.array_equal(outgoing_props, jnp.array([True], dtype=bool))
+
+    # Update the angle for the incoming medium using JAX's conditional function (jax.lax.cond)
+    # If the condition is true, the theta array is modified for the incoming medium
+    theta_array = cond(condition_incoming, update_theta_arr_incoming, return_unchanged_theta, operand=theta_array)
+
+    # Update the angle for the outgoing medium using JAX's conditional function (jax.lax.cond)
+    # If the condition is true, the theta array is modified for the outgoing medium
+    theta_array = cond(condition_outgoing, update_theta_arr_outgoing, return_unchanged_theta, operand=theta_array)
+
+    # Return a 1D theta array for each layer
+    # This array contains the calculated angles for all layers
+    return theta_array
+
+def compute_layer_angles(angle_of_incidence: ArrayLike,
+                         nk_list: ArrayLike,
+                         polarization: ArrayLike) -> Array:
+    """
+    This function calculates the angles of wave propagation within a multilayer system 
+    for a given angle of incidence and polarization state. It selects the appropriate 
+    function to compute the layer angles based on the type of polarization (s-polarization or p-polarization).
+    The function uses `jnp.select` to dynamically choose the appropriate subfunction
+    (`compute_layer_angles_s_pol` or `compute_layer_angles_p_pol`) based on the value of the `polarization` array. 
+    These subfunctions are responsible for computing the angles for s-polarization or p-polarization, respectively.
+
+    Arguments:
+    - angle_of_incidence (ArrayLike): The angle of incidence of the incoming wave, given as a scalar value. 
+      This angle is typically measured relative to the normal of the first interface in the multilayer structure.
+    - nk_list (ArrayLike): A JAX array containing the complex refractive indices for all layers in the 
+      multilayer system. The array structure is as follows:
+        [n_incoming, n_layer_0, n_layer_1, ..., n_layer_N, n_outgoing], where:
+        - `n_incoming` is the refractive index of the incoming medium,
+        - `n_layer_0, n_layer_1, ..., n_layer_N` are the refractive indices of the individual layers, and
+        - `n_outgoing` is the refractive index of the outgoing medium.
+      The refractive indices can be complex, where the real part represents the refractive index 
+      and the imaginary part accounts for material absorption (extinction coefficient).
+    - polarization (ArrayLike): A binary JAX array indicating the type of polarization:
+        - If the array is [False], it represents s-polarization, where the electric field 
+          is perpendicular to the plane of incidence.
+        - If the array is [True], it represents p-polarization, where the electric field 
+          is parallel to the plane of incidence.
+
+    Returns:
+    - Array: A JAX array containing the calculated angles of wave propagation within each layer of the multilayer 
+      structure. The specific angles depend on the type of polarization and the refractive indices of the layers.
+    """
+    
+    return jnp.select(
+        condlist=[
+            jnp.array_equal(polarization, jnp.array([False], dtype=bool)),  # Check for s-polarization
+            jnp.array_equal(polarization, jnp.array([True], dtype=bool))   # Check for p-polarization
+        ],
+        choicelist=[
+            compute_layer_angles_s_pol(angle_of_incidence, nk_list),  # Call the s-polarization function
+            compute_layer_angles_p_pol(angle_of_incidence, nk_list)   # Call the p-polarization function
+        ]
+    )
\ No newline at end of file
diff --git a/tmmax/cascaded_matmul.py b/tmmax/cascaded_matmul.py
new file mode 100644
index 0000000..3c4b979
--- /dev/null
+++ b/tmmax/cascaded_matmul.py
@@ -0,0 +1,250 @@
+import jax.numpy as jnp  # Import JAX's version of NumPy
+from jax.lax import scan  # Import the `scan` function, which allows efficient looping and state updates in jax.ndarray computations
+from jax import Array  # Import the `Array` type from JAX for precise type hinting with JAX arrays
+from jax.typing import ArrayLike  # Import `ArrayLike` for type hinting, accommodating various array-like structures compatible with JAX
+
+def compute_first_layer_matrix_coherent(r: ArrayLike, t: ArrayLike) -> Array:
+    """
+    This function calculates the transfer matrix for the interface between the incoming and the first layer of the
+    multilayer coating. This function assumes the first layer is coherent and should not be used 
+    if the first layer is not coherent.
+
+    Arguments:
+    - r (ArrayLike): The reflection coefficient at the interface between the incoming medium and the first layer of the thin film
+    - t (ArrayLike): The transmission coefficient at the interface between the incoming medium and the first layer of the thin film
+
+    Returns:
+    - first_layer_matrix (Array): A 2x2 matrix representing the transfer matrix for the first layer at the interface 
+    between the incoming and outgoing medium, adjusted by the transmission coefficient t.
+    """
+    # Create a 2x2 matrix representing the reflection and transmission coefficients
+    # The matrix structure is [[1, r], [r, 1]], where r is the reflection coefficient.
+    first_layer_matrix = jnp.array([[1, r],
+                                    [r, 1]])
+
+    # Scale the matrix by dividing each element by the transmission coefficient t.
+    # This ensures proper normalization of the transfer matrix with respect to t.
+    first_layer_matrix = jnp.multiply(jnp.true_divide(1, t), first_layer_matrix)
+
+    # Return the adjusted transfer matrix for the first layer.
+    # This matrix describes the interface between the incoming and outgoing media.
+    return first_layer_matrix
+
+def compute_first_layer_matrix_incoherent(r_forward: ArrayLike, t_forward: ArrayLike, 
+                                          r_backward: ArrayLike, t_backward: ArrayLike):
+    """
+    This function calculates the transfer matrix for the interface between the incoming and the first layer of the
+    multilayer coating where the first layer is incoherent.
+
+    Arguments:
+    - r_forward (ArrayLike): The reflection coefficient for the forward direction at the interface between 
+    the incoming medium and the first layer.
+    - t_forward (ArrayLike): The transmission coefficient for the forward direction at the interface between 
+    the incoming medium and the first layer.
+    - r_backward (ArrayLike): The reflection coefficient for the backward direction at the interface between 
+    the incoming medium and the first layer.
+    - t_backward (ArrayLike): The transmission coefficient for the backward direction at the interface between 
+    the incoming medium and the first layer.
+
+    Returns:
+    - first_layer_matrix (Array): A 2x2 matrix representing the transfer matrix for the first layer at the interface 
+    between the incoming medium and the first layer, adjusted by the forward transmission and reflection coefficients
+    """
+    # Initialize the element at position (0, 0) of the transfer matrix
+    transfer_matrix_00 = 1
+    
+    # Calculate the element at position (0, 1) of the transfer matrix using the backward reflection coefficient
+    transfer_matrix_01 = jnp.multiply(-1, r_backward)
+    
+    # Calculate the element at position (1, 0) of the transfer matrix using the forward reflection coefficient
+    transfer_matrix_10 = r_forward
+    
+    # Calculate the element at position (1, 1) using the forward and backward transmission coefficients,
+    # and the forward and backward reflection coefficients
+    transfer_matrix_11 = jnp.subtract(jnp.multiply(t_forward, t_backward), jnp.multiply(r_forward, r_backward))
+
+    # Construct the 2x2 transfer matrix from the calculated elements
+    first_layer_matrix = jnp.array([[transfer_matrix_00, transfer_matrix_01],
+                                    [transfer_matrix_10, transfer_matrix_11]])
+
+    # Scale the entire matrix by dividing each element by the forward transmission coefficient t_forward
+    first_layer_matrix = jnp.multiply(jnp.true_divide(1, t_forward), first_layer_matrix)
+
+    # Return the adjusted transfer matrix for the first layer
+    return first_layer_matrix
+
+def coh_matmul(carry: ArrayLike, phase_r_t: ArrayLike):
+    """
+    This function performs a single step of matrix multiplication in the transfer matrix method for coherent thin films.
+    It constructs the transfer matrix M_i using the given phase, reflection, and transmission coefficients, and then multiplies 
+    it with the accumulated matrix carry (representing M_carry). This is part of the iterative process of computing the overall 
+    transfer matrix M = M_1 @ M_2 @ ... @ M_N in a computationally efficient manner using JAX.
+
+    Arguments:
+    carry: ArrayLike
+        The matrix that accumulates the multiplication process in the transfer matrix method. 
+        It represents the product of all previous transfer matrices (M_carry in the method).
+    phase_r_t: ArrayLike
+        A 1D array containing three elements:
+        - phase_r_t[0]: A phase term used to compute the exponential factors in the transfer matrix. 
+          This is a real number representing the phase in radians.
+        - phase_r_t[1]: A reflection coefficient (r). It represents the ratio of reflected wave amplitude 
+          to the incident wave amplitude at the interface.
+        - phase_r_t[2]: A transmission coefficient (t). It represents the ratio of transmitted wave amplitude 
+          to the incident wave amplitude at the interface.
+
+    Returns:
+    result: tuple
+        A tuple containing two identical elements:
+        - The updated matrix after multiplying the current transfer matrix (M_i) with the accumulated matrix (M_carry).
+        - A placeholder for `jax.lax.scan`, which requires a tuple to function correctly.
+    """
+
+    # Compute the (0,0) element of the transfer matrix: exp(-i * phase) / t
+    transfer_matrix_00 = jnp.exp(jnp.multiply(jnp.array([-1j], dtype = jnp.complex64), phase_r_t.at[0].get()))
+
+    # Compute the (1,1) element of the transfer matrix: exp(i * phase) / t
+    transfer_matrix_11 = jnp.exp(jnp.multiply(jnp.array([1j], dtype = jnp.complex64), phase_r_t.at[0].get()))
+
+    # Compute the (0,1) element of the transfer matrix: r * exp(-i * phase) / t
+    transfer_matrix_01 = jnp.multiply(phase_r_t.at[1].get(), transfer_matrix_00)
+
+    # Compute the (1,0) element of the transfer matrix: r * exp(i * phase) / t
+    transfer_matrix_10 = jnp.multiply(phase_r_t.at[1].get(), transfer_matrix_11)
+
+    # Assemble the full 2x2 transfer matrix M_i
+    # M_i = (1/t) * [[exp(-i * phase), r * exp(-i * phase)],
+    #                [r * exp(i * phase), exp(i * phase)]]
+    transfer_matrix = jnp.multiply(jnp.true_divide(1, phase_r_t.at[2].get()), jnp.array([[transfer_matrix_00, transfer_matrix_01],
+                                                                                         [transfer_matrix_10, transfer_matrix_11]]))
+    
+    # Multiply the accumulated matrix (carry) with the current transfer matrix (M_i)
+    result = jnp.squeeze(jnp.matmul(carry, transfer_matrix))
+
+    # Return the updated matrix and a placeholder (same matrix) for jax.lax.scan compatibility
+    return result, result
+
+def coh_cascaded_matrix_multiplication(phases: ArrayLike, rts: ArrayLike) -> Array:
+    """
+    This function calculates the total transfer matrix of a multilayer thin-film structure by performing a cascaded matrix multiplication. 
+    It takes in phase shifts and reflection/transmission coefficients for each layer and computes the overall transfer coefficient matrix. 
+    Arguments:
+        phases (ArrayLike): 
+            A 1D array of size N (number of layers) representing the accumulated phase shift in each layer of the multilayer structure. 
+            Each entry corresponds to the phase shift of the wave as it passes through the respective layer.
+
+        rts (ArrayLike): 
+            A 2D array of size [N, 2], where N is the number of layers. 
+            For the i-th layer:
+                rts[i, 0] represents the reflection coefficient `r` for the i+1-th layer.
+                rts[i, 1] represents the transmission coefficient `t` for the i+1-th layer.
+                rts[-1, :] represents the transmission and reflection coefficients of the Nth layer and outgoing medium interface.
+
+    Returns:
+        Array: 
+            A 2x2 complex matrix representing the total transfer matrix of the multilayer system (except incoming r matrix). 
+            This matrix encapsulates the overall effect of all the layers in terms of phase shifts, reflection, and transmission.
+    """
+    # Concatenate the phases (1D array) with the r and t coefficients (2D array) along a new axis
+    # This creates a 2D array where each row represents a layer, and each row contains:
+    # [phase, r, t] values for that layer.
+    phase_rt_stack = jnp.concat([jnp.expand_dims(phases, 1), rts], axis=1)
+
+    # Initialize the result as a 2x2 identity matrix of complex numbers
+    # The identity matrix serves as the neutral element for matrix multiplication.
+    initial_value = jnp.eye(2, dtype=jnp.complex64)
+
+    # Use the `lax.scan` function to iteratively multiply matrices over the layers
+    # `coh_matmul` is a user-defined function (not shown here) that performs the matrix multiplication for a single layer.
+    # `initial_value` is the starting matrix, and `phase_rt_stack` provides the data for each step.
+    # The scan function returns the final accumulated matrix in `result`.
+    result, _ = scan(coh_matmul, initial_value, phase_rt_stack)  # Scan function accumulates results of coh_matmul over the matrices.
+
+    # Return the resulting 2x2 matrix, representing the total transfer matrix for the multilayer structure.
+    return result
+
+def incoh_matmul(carry: ArrayLike, pass_rf_tf_rb_tb: ArrayLike):
+    """ 
+    This function performs a single step of matrix multiplication in the transfer matrix method for coherent thin films.
+    It constructs the transfer matrix M_i using the given phase, reflection, and transmission coefficients, and then multiplies 
+    it with the accumulated matrix carry (representing M_carry). This is part of the iterative process of computing the overall 
+    transfer matrix M = M_1 @ M_2 @ ... @ M_N in a computationally efficient manner using JAX.
+
+    Arguments:
+    - carry (ArrayLike): A 2x2 matrix representing the accumulated transfer matrix from previous layers.
+    - pass_rf_tf_rb_tb (ArrayLike): A 1D array containing reflection, transmission, and passing ratio coefficients 
+    required to construct the transfer matrix:
+    * pass_rf_tf_rb_tb[0]: Passing ratio
+    * pass_rf_tf_rb_tb[1]: Forward reflection coefficient (r_f)
+    * pass_rf_tf_rb_tb[2]: Forward transmission coefficient (t_f)
+    * pass_rf_tf_rb_tb[3]: Backward reflection coefficient (r_b)
+    * pass_rf_tf_rb_tb[4]: Backward transmission coefficient (t_b)
+
+    Returns:
+    - result (ArrayLike): Updated accumulated transfer matrix after multiplication.
+    - result (ArrayLike): The same updated transfer matrix is returned twice for JAX compatibility.
+    """
+
+    # Compute the first element of the transfer matrix: 1 / passing ratio
+    transfer_matrix_00 = jnp.true_divide(1, pass_rf_tf_rb_tb.at[0].get())
+
+    # Compute the second element of the transfer matrix: -r_b / passing ratio
+    transfer_matrix_01 = jnp.true_divide(jnp.multiply(-1, pass_rf_tf_rb_tb.at[3].get()), pass_rf_tf_rb_tb.at[0].get())
+
+    # Compute the third element of the transfer matrix: passing ratio * r_f
+    transfer_matrix_10 = jnp.multiply(pass_rf_tf_rb_tb.at[0].get(), pass_rf_tf_rb_tb.at[1].get())
+
+    # Compute the fourth element of the transfer matrix
+    # passing ratio * (t_f * t_b - r_f * r_b)
+    transfer_matrix_11 = jnp.multiply(pass_rf_tf_rb_tb.at[0].get(), 
+                                      jnp.subtract(jnp.multiply(pass_rf_tf_rb_tb.at[2].get(), pass_rf_tf_rb_tb.at[4].get()), 
+                                                   jnp.multiply(pass_rf_tf_rb_tb.at[1].get(), pass_rf_tf_rb_tb.at[3].get())))
+
+    # Construct the full transfer matrix M_i
+    transfer_matrix = jnp.multiply(jnp.true_divide(1, pass_rf_tf_rb_tb.at[2].get()), 
+                                   jnp.array([[transfer_matrix_00, transfer_matrix_01], 
+                                              [transfer_matrix_10, transfer_matrix_11]]))
+
+    # Multiply the accumulated transfer matrix with the current layer's transfer matrix
+    result = jnp.matmul(carry, transfer_matrix)
+
+    # Remove unnecessary dimensions if applicable
+    result = jnp.squeeze(result)
+
+    # Return the updated transfer matrix twice (likely for JAX compatibility in a scan function)
+    return result, result
+
+def incoh_cascaded_matrix_multiplication(forward_magnitudes: ArrayLike, backward_magnitudes: ArrayLike, layer_passes: ArrayLike) -> Array:
+    """
+    This function performs cascaded matrix multiplication for an incoherent optical system using JAX.
+    It iterates through layer-wise power distributions and magnitudes, accumulating their effects via matrix multiplication.
+
+    Arguments:
+    forward_magnitudes (ArrayLike):
+        A 2D array of shape [N, 2] where N is the number of layers.
+        Each row contains the forward-traveling field magnitudes for the corresponding layer.
+
+    backward_magnitudes (ArrayLike):
+        A 2D array of shape [N, 2] where N is the number of layers.
+        Each row contains the backward-traveling field magnitudes for the corresponding layer.
+
+    layer_passes (ArrayLike):
+        A 1D array of length N representing the passing fractions associated with each layer.
+        These values are the  fraction of light that successfully passes through the incoherent layers
+
+    Returns:
+    Array:
+        A 2x2 matrix representing the accumulated effect of the layers in an incoherent system.
+        The resulting matrix encodes the system's overall transmission and reflection characteristics.
+    """
+    # Stack the layer power distribution, forward magnitudes, and backward magnitudes into a single array
+    pass_rf_tf_rb_tb_stack = jnp.concat([jnp.expand_dims(layer_passes, 1), forward_magnitudes, backward_magnitudes], axis=1)
+
+    # Initialize the matrix as a 2x2 identity matrix (represents no transformation initially)
+    initial_value = jnp.eye(2, dtype=jnp.float32)
+
+    # Perform a scan (folding operation) over layers, applying matmul_incoherent iteratively
+    result, _ = scan(incoh_matmul, initial_value, pass_rf_tf_rb_tb_stack)
+
+    # Return the final accumulated matrix, representing the total effect of the multilayer system
+    return result
diff --git a/tmmax/data.py b/tmmax/data.py
new file mode 100644
index 0000000..e248e71
--- /dev/null
+++ b/tmmax/data.py
@@ -0,0 +1,641 @@
+import jax.numpy as jnp # Import the jax numpy module for numerical and mathematical operations
+from jax import vmap, grad # Import JAX functions for function gradient, vectorization
+from jax.lax import fori_loop # This allows us to implement efficient and JIT compiled for-loops
+from jax import Array  # Import the Array class from jax for type specification purpose.
+from jax.typing import ArrayLike  # Import ArrayLike from jax.typing. It is for safely casting to a JAX array
+import matplotlib.pyplot as plt # Import matplotlib for plotting
+import numpy as np # Importing numpy lib for savetxt function for saving arrays to csv files
+import os # Importing os to handle file paths
+import pandas as pd # Importing pandas to handle CSV data
+from typing import Union, List, Tuple, Optional, Callable, Dict # Type hints for function signatures
+import warnings # Importing the warnings module to handle warnings in the code
+
+from . import nk_data_dir
+
+def load_nk_data_csv(material_name: str = '') -> Union[jnp.ndarray, None]:
+    """
+    Load the refractive index (n) and extinction coefficient (k) data for a given material: (n + 1j * k).
+
+    This function fetches wavelength-dependent refractive index (n) and extinction coefficient (k)
+    data for a specified material. The data is read from a CSV file located in the 'nk_data/' directory.
+    The CSV file should be named after the material, e.g., 'Si.csv', and include three columns: wavelength (in micrometers),
+    refractive index (n), and extinction coefficient (k). These parameters are crucial for optical simulations,
+    allowing the user to work with materials' optical properties over a range of wavelengths.
+
+    Args:
+        material_name (str): The name of the material for which the data is to be loaded.
+                             This must not be an empty string, and the corresponding CSV file
+                             must exist in the 'nk_data/' directory.
+
+    Returns:
+        jnp.ndarray: A 2D array containing the wavelength (first column),
+                     refractive index (n) (second column), and extinction coefficient (k) (third column).
+                     Each row corresponds to a different wavelength.
+
+        None: If the function fails due to any raised exception or if the CSV file is empty,
+              it will return None.
+
+    Raises:
+        ValueError: If the material name is an empty string.
+        FileNotFoundError: If the file for the given material does not exist in the 'nk_data/' folder.
+        IOError: If there's an issue reading or parsing the file.
+    """
+    # Check that the material name is not an empty string
+    if not material_name:
+        raise ValueError("Material name cannot be an empty string.")  # Raise an error if no material is provided
+
+    # Construct the file path and check if the file exists
+    file_path = os.path.join(nk_data_dir, f'{material_name}.csv')  # Create the full path to the file
+    if not os.path.exists(file_path):
+        # Raise an error if the file for the material does not exist
+        raise FileNotFoundError(f"No data found for material '{material_name}' in 'nk_data/' folder (library database).")
+
+    # Load the data from the CSV file
+    try:
+        # Load the CSV data as a JAX array (important for using JAX's functionality, like automatic differentiation)
+        data = jnp.asarray(pd.read_csv(file_path, skiprows=1, header=None).values)
+    except Exception as e:
+        # If an error occurs during file reading or conversion, raise an IOError
+        raise IOError(f"An error occurred while loading data for '{material_name}': {e}")
+
+    # Check if the file is empty or doesn't contain valid data
+    if data.size == 0:
+        # Raise an error if the data array is empty or incorrectly formatted
+        raise ValueError(f"The file for material '{material_name}' is empty or not in the expected format.")
+
+    return data  # Return the loaded data as a JAX array
+
+def load_nk_data_numpy(material_name: str = '') -> Union[jnp.ndarray, None]:
+    """
+    Load the refractive index (n) and extinction coefficient (k) data for a given material: (n + 1j * k).
+
+    This function fetches wavelength-dependent refractive index (n) and extinction coefficient (k)
+    data for a specified material. The data is read from a CSV file located in the 'nk_data/' directory.
+    The CSV file should be named after the material, e.g., 'Si.csv', and include three columns: wavelength (in micrometers),
+    refractive index (n), and extinction coefficient (k). These parameters are crucial for optical simulations,
+    allowing the user to work with materials' optical properties over a range of wavelengths.
+
+    Args:
+        material_name (str): The name of the material for which the data is to be loaded.
+                             This must not be an empty string, and the corresponding CSV file
+                             must exist in the 'nk_data/' directory.
+
+    Returns:
+        jnp.ndarray: A 2D array containing the wavelength (first column),
+                     refractive index (n) (second column), and extinction coefficient (k) (third column).
+                     Each row corresponds to a different wavelength.
+
+        None: If the function fails due to any raised exception or if the CSV file is empty,
+              it will return None.
+
+    Raises:
+        ValueError: If the material name is an empty string.
+        FileNotFoundError: If the file for the given material does not exist in the 'nk_data/' folder.
+        IOError: If there's an issue reading or parsing the file.
+    """
+    # Check that the material name is not an empty string
+    if not material_name:
+        raise ValueError("Material name cannot be an empty string.")  # Raise an error if no material is provided
+
+    # Construct the file path and check if the file exists
+    file_path = os.path.join(nk_data_dir, f'numpy/{material_name}.npy')  # Create the full path to the file
+    if not os.path.exists(file_path):
+        # Raise an error if the file for the material does not exist
+        raise FileNotFoundError(f"No data found for material '{material_name}' in 'nk_data/numpy/' folder (library database).")
+
+    # Load the data from the CSV file
+    try:
+        # Load the CSV data as a JAX array (important for using JAX's functionality, like automatic differentiation)
+        data = jnp.load(file_path)
+
+    except Exception as e:
+        # If an error occurs during file reading or conversion, raise an IOError
+        raise IOError(f"An error occurred while loading data for '{material_name}': {e}")
+
+    # Check if the file is empty or doesn't contain valid data
+    if data.size == 0:
+        # Raise an error if the data array is empty or incorrectly formatted
+        raise ValueError(f"The file for material '{material_name}' is empty or not in the expected format.")
+
+    return data  # Return the loaded data as a JAX array
+
+def interpolate_1d(x: jnp.ndarray, y: jnp.ndarray) -> Callable[[float], float]:
+    """
+    Creates a 1D linear interpolation function based on the provided x and y arrays.
+
+    This function returns a callable that performs linear interpolation on the input data points (x, y).
+    Given an x value, it finds the corresponding y value by assuming a straight line between two closest points
+    in the x array and using the equation of the line.
+
+    Args:
+        x (jnp.ndarray): Array of x values (independent variable). It must be sorted in ascending order.
+        y (jnp.ndarray): Array of y values (dependent variable). It should have the same length as the x array.
+
+    Returns:
+        Callable[[float], float]: A function that, when provided with a single float x value, returns the corresponding
+        interpolated float y value based on the linear interpolation.
+    """
+
+    def interpolate(x_val: float) -> float:
+        # Find the index where x_val would fit in x to maintain the sorted order
+        idx = jnp.searchsorted(x, x_val, side='right') - 1
+        # Ensure idx is within valid bounds (0 to len(x)-2) to avoid out-of-bounds errors
+        idx = jnp.clip(idx, 0, x.shape[0] - 2)
+
+        # Retrieve the two nearest x values, x_i and x_{i+1}, that surround x_val
+        x_i, x_ip1 = x[idx], x[idx + 1]
+        # Retrieve the corresponding y values, y_i and y_{i+1}, at those x positions
+        y_i, y_ip1 = y[idx], y[idx + 1]
+
+        # Calculate the slope of the line between (x_i, y_i) and (x_{i+1}, y_{i+1})
+        slope = (y_ip1 - y_i) / (x_ip1 - x_i)
+
+        # Interpolate the y value using the slope formula: y = y_i + slope * (x_val - x_i)
+        return y_i + slope * (x_val - x_i)
+
+    return interpolate  # Return the interpolation function to be used later
+
+def interpolate_nk(material_name: str) -> Callable[[float], complex]:
+    """
+    Load the nk data for a given material and return a callable function that computes
+    the complex refractive index for any wavelength.
+
+    Args:
+        material_name (str): Name of the material to load the nk data for.
+
+    Returns:
+        Callable[[float], complex]: A function that takes a wavelength (in meters) and
+                                    returns the complex refractive index.
+    """
+    nk_data = load_nk_data_numpy(material_name)  # Load the nk data for the specified material
+    wavelength, refractive_index, extinction_coefficient = nk_data[0,:], nk_data[1,:], nk_data[2,:]  # Transpose to get columns as variables
+
+    # Interpolate refractive index and extinction coefficient
+    compute_refractive_index = interpolate_1d(wavelength * 1e-6, refractive_index)  # Convert wavelength to meters for interpolation
+    compute_extinction_coefficient = interpolate_1d(wavelength * 1e-6, extinction_coefficient)  # Convert wavelength to meters for interpolation
+
+
+    def compute_nk(wavelength: float) -> complex:
+        """
+        Compute the complex refractive index for a given wavelength.
+
+        Args:
+            wavelength (float): Wavelength in meters.
+
+        Returns:
+            complex: The complex refractive index, n + i*k, where n is the refractive index
+                     and k is the extinction coefficient.
+        """
+        n = compute_refractive_index(wavelength)  # Get the refractive index at the given wavelength
+        k = compute_extinction_coefficient(wavelength)  # Get the extinction coefficient at the given wavelength
+        return jnp.array(n + 1j * k)  # Combine n and k into a complex number and return
+
+    return compute_nk  # Return the function that computes the complex refractive index
+
+def repeat_last_element(array: ArrayLike, 
+                        repeat: ArrayLike) -> Array:
+    """
+    The function appends (or pads) the last element of the input `array` to the 
+    end of the array `repeat` number of times. It uses the `jnp.pad` function 
+    from the JAX library to perform padding, which allows for efficient and 
+    differentiable operations on the array.
+
+    Arguments:
+    - `array`: An input array-like object that contains numeric or other elements. 
+       It is the array on which the operation will be performed.
+    - `repeat`: A numeric value that specifies how many times the last element of 
+       the `array` should be appended to the end.
+
+    Returns:
+    - A new array where the last element of the input `array` is repeated 
+      `repeat` number of times and appended to the original array. 
+      The returned array is padded with the repeated values of the last element.
+    """
+
+    # Padding is applied at the end of the array (0 padding at the start, and `repeat` padding at the end).
+    # The padding mode is set to 'constant', meaning a constant value will be used for padding.
+    # `array.at[-1].get()` retrieves the last element of the input array to use as the constant padding value.
+    padded_array = jnp.pad(array, (0, repeat), mode='constant', constant_values=array.at[-1].get())
+    
+    # Return the modified array after appending the repeated last element.
+    return padded_array
+
+def prepare_material_data(data_container: List, 
+                          num_of_materials: int, 
+                          max_dim: int) -> Array:
+    """
+    This function processes a list of material data and standardizes their dimensions 
+    by padding or repeating data to match the maximum data length (`max_dim`) among all materials.
+
+    Arguments:
+    - data_container: List
+        A list containing material data for multiple materials. 
+        Each element in this list is a 2D array where:
+          - Row 0 represents the wavelength data (`wl`) for the material.
+          - Row 1 represents the refractive index (`n`) data for the material.
+          - Row 2 represents the extinction coefficient (`k`) data for the material.
+        Each material may have a different number of data points.
+    
+    - num_of_materials: int
+        The total number of materials in the `data_container`.
+        This determines the number of iterations required to process all materials.
+
+    - max_dim: int
+        The maximum length of the data arrays (i.e., the number of data points) 
+        across all materials. All materials' data will be padded or repeated to match this length.
+
+    Returns:
+    - Array: A 3D JAX array of shape `(num_of_materials, 3, max_dim)` containing:
+        - For each material, the first dimension stores the wavelength data (in micrometers).
+        - The second dimension stores the refractive index (`n`) data.
+        - The third dimension stores the extinction coefficient (`k`) data.
+      The function ensures all data arrays are the same size by repeating the last values as necessary.
+    """
+
+    # Create an empty 3D array with shape (num_of_materials, 3, max_dim)
+    # This array will store the wavelength, n, and k data for all materials after processing.
+    data = jnp.empty((num_of_materials, 3, max_dim))
+    
+    # Loop through each material in the data_container to process its data.
+    for i in range(num_of_materials):
+        # Retrieve the data for the ith material from the data_container.
+        material_data = data_container[i]
+        
+        # Calculate the number of elements to repeat, which is the difference 
+        # between max_dim and the current material's data length.
+        repeat = max_dim - (jnp.shape(material_data)[1])
+        
+        # Repeat the last element of the wavelength data to fill up to max_dim.
+        wl_at_i = repeat_last_element(material_data.at[0, :].get(), repeat)
+        
+        # Repeat the last element of the refractive index (n) data to fill up to max_dim.
+        n_at_i = repeat_last_element(material_data.at[1, :].get(), repeat)
+        
+        # Repeat the last element of the extinction coefficient (k) data to fill up to max_dim.
+        k_at_i = repeat_last_element(material_data.at[2, :].get(), repeat)
+        
+        # Store the processed wavelength data (converted from micrometers to meters).
+        data = data.at[i, 0, :].set(jnp.multiply(wl_at_i, 1e-6))
+        
+        # Store the processed refractive index (n) data into the 3D array.
+        data = data.at[i, 1, :].set(n_at_i)
+        
+        # Store the processed extinction coefficient (k) data into the 3D array.
+        data = data.at[i, 2, :].set(k_at_i)
+    
+    # Return the fully processed 3D array with standardized dimensions for all materials.
+    return data
+
+def create_data(material_set: List[str]) -> Array:
+    """
+    This function loads material data for a given list of materials and prepares a 3D JAX array containing the material properties. 
+    The function iterates over the provided material set, loads corresponding data files, and calculates the maximum dimensionality 
+    of the data across all materials. It then uses a helper function `prepare_material_data` to organize the material data into 
+    a structured format.
+
+    Arguments:
+    - `material_set` (List[str]): A list of strings where each string represents the name of a material. These names are used to 
+      locate and load the corresponding material data from a predefined directory.
+
+    Returns:
+    - A JAX array of shape `(num_of_materials, 3, max_dim)` where:
+      - `num_of_materials` is the number of materials in the provided material set.
+      - `3` represents three columns of material properties (e.g., wavelength, refractive index, extinction coefficient).
+      - `max_dim` is the maximum number of data points among all materials.
+    """
+
+    # Initialize the maximum dimension of material data to zero.
+    max_dim = 0
+
+    # Create an empty list to store data for each material.
+    data_container = []
+
+    # Calculate the total number of materials provided in the material set.
+    num_of_materials = len(material_set)
+
+    # Loop through each material in the material set.
+    for i in range(num_of_materials):
+        # Construct the file path for the current material using its name and the directory structure.
+        file_path = os.path.join(nk_data_dir, f'numpy/{material_set[i]}.npy')
+
+        # Load the material data from the .npy file as a JAX array.
+        material_data = jnp.load(file_path)
+
+        # Append the loaded material data to the container list.
+        data_container.append(material_data)
+
+        # Check if the second dimension of the current material's data exceeds the current max dimension.
+        if jnp.shape(material_data)[1] > max_dim:
+            # Update the maximum dimension if the current material has more data points.
+            max_dim = jnp.shape(material_data)[1]
+
+    # Call a helper function to prepare the material data into a structured JAX array.
+    # Pass the list of material data, the number of materials, and the maximum dimension.
+    data = prepare_material_data(data_container, num_of_materials, max_dim)
+
+    # Return the prepared 3D JAX array containing all the material data.
+    return data
+
+def create_nk_list(material_distribution: ArrayLike, 
+                   data: ArrayLike, 
+                   wavelength: ArrayLike) -> Array:
+    """
+    This function generates a list of complex refractive indices (nk) for a multilayer thin film 
+    structure based on the provided material distribution, wavelength, and material data. 
+
+    Arguments:
+    - material_distribution: A JAX array of integers that indicates the material index for each 
+      layer of the multilayer thin film. For example, if `material_distribution[1]` is 1, 
+      it implies that the first layer corresponds to the material in the material set at index 1.
+
+    - data: A 3D JAX array containing the wavelength-dependent refractive index (n) and extinction 
+      coefficient (k) for all materials in the material set. The array has the shape 
+      (num_of_materials, 3, max_dim), where:
+        * `num_of_materials` is the total number of materials in the material set.
+        * The first dimension (index 0) stores the wavelength values.
+        * The second dimension (index 1) stores the refractive indices (n).
+        * The third dimension (index 2) stores the extinction coefficients (k).
+      
+    - wavelength: A scalar that represents wavelength (in meters) at which the refractive index and 
+      extinction coefficient will be interpolated.
+
+    Returns:
+    - A JAX array of the same length as `material_distribution`, where each element represents 
+      the complex refractive index (n + i*k) of the corresponding layer at the specified wavelength.
+    """
+
+    def update_nk_list(i, nk_carry):
+        # Retrieve the index of the material for the i-th layer from material_distribution
+        material_data_idx = material_distribution.at[i].get()
+
+        # Interpolate the refractive index (n) for the i-th layer at the given wavelength
+        n_at_i = jnp.interp(wavelength, 
+                            data.at[material_data_idx, 0, :].get(), 
+                            data.at[material_data_idx, 1, :].get())
+        
+        # Interpolate the extinction coefficient (k) for the i-th layer at the given wavelength
+        k_at_i = jnp.interp(wavelength, 
+                            data.at[material_data_idx, 0, :].get(), 
+                            data.at[material_data_idx, 2, :].get())
+        
+        # Compute the complex refractive index (nk = n + i*k) for the i-th layer
+        nk_at_i = jnp.add(n_at_i, jnp.multiply(1j, k_at_i))
+        
+        # Update the nk_carry array with the computed nk value for the i-th layer
+        nk_carry = nk_carry.at[i].set(nk_at_i)
+        
+        # Return the updated nk_carry array
+        return nk_carry
+
+    # Initialize the nk_carry array as a complex64 array with the same size as material_distribution
+    # Use the fori_loop to iterate over all layers and populate the nk_carry array with nk values
+    complete_nk_list = fori_loop(0, 
+                                 len(material_distribution), 
+                                 update_nk_list, 
+                                 material_distribution.astype('complex64'))
+    
+    # Return the complete list of complex refractive indices for all layers
+    return complete_nk_list
+
+def material_distribution_to_set(material_distribution: List[str]) -> Tuple:
+    """
+    This function takes a list of material names representing a multilayer thin film 
+    and converts it into two outputs: a unique set of materials and a corresponding list 
+    of integers that represent the materials by their enumerated indices. This is useful 
+    for simplifying the representation of material sequences in numerical simulations 
+    or computations. For example, a material sequence `["SiO2", "TiO2", "Al2O3", "TiO2", 
+    "Al2O3", "SiO2"]` will be converted into a material set `["SiO2", "TiO2", "Al2O3"]` 
+    and an enumerated list `[0, 1, 2, 1, 2, 0]`.
+
+    Arguments:
+    material_distribution: List[str]
+        A list of strings where each string represents the name of a material 
+        in a multilayer thin film. The list may contain duplicate entries, as it 
+        reflects the sequence of materials in the structure.
+
+    Returns:
+    Tuple[List[str], jax.numpy.ndarray]
+        - A list of unique material names, representing all distinct materials used 
+          in the input `material_distribution`.
+        - A JAX NumPy array of integers where each integer corresponds to the 
+          index of a material in the unique material set. The order of these integers 
+          reflects the order of materials in the input `material_distribution`.
+    """
+
+    # Create a unique list of materials by converting the input list to a set (removing duplicates)
+    # and then back to a list to preserve order.
+    material_set = list(set(material_distribution))
+
+    # Create a dictionary where each material in the set is assigned a unique integer index.
+    # This will allow mapping from material names to integers.
+    material_enum = {material: i for i, material in enumerate(material_set)}
+
+    # Map each material in the input list to its corresponding integer index in `material_enum`.
+    # The result is a list of integers that mirrors the input material distribution in order.
+    material_list = jnp.array([int(material_enum[material]) for material in material_distribution])
+
+    # Return the unique material set and the corresponding enumerated material list.
+    return material_set, material_list
+
+def add_material_to_nk_database(wavelength_arr, refractive_index_arr, extinction_coeff_arr, material_name=''):
+    """
+    Add material properties to the nk database by saving the data into a CSV file.
+
+    This function validates and saves material properties such as wavelength, refractive index,
+    and extinction coefficient into a CSV file. The file is named based on the provided material name.
+
+    Args:
+        wavelength_arr (jnp.ndarray): Array of wavelengths in micrometers.
+        refractive_index_arr (jnp.ndarray): Array of refractive indices corresponding to the wavelengths.
+        extinction_coeff_arr (jnp.ndarray): Array of extinction coefficients corresponding to the wavelengths.
+        material_name (str): The name of the material, which is used to name the output CSV file.
+
+    Raises:
+        TypeError: If any of the input arrays are not of type jax.numpy.ndarray.
+        ValueError: If the input arrays have different lengths or if the material name is empty.
+    """
+
+    # Validate input types
+    # Check if all input arrays are of type jax.numpy.ndarray
+    if not all(isinstance(arr, jnp.ndarray) for arr in [wavelength_arr, refractive_index_arr, extinction_coeff_arr]):
+        raise TypeError("All input arrays must be of type jax.numpy.ndarray")
+
+    # Ensure all arrays have the same length
+    # Check if the length of refractive_index_arr and extinction_coeff_arr match wavelength_arr
+    if not all(len(arr) == len(wavelength_arr) for arr in [refractive_index_arr, extinction_coeff_arr]):
+        raise ValueError("All input arrays must have the same length")
+
+    # Validate material name
+    # Ensure that the material name is not an empty string
+    if not material_name.strip():
+        raise ValueError("Material name cannot be an empty string")
+
+    # Check for extinction coefficients greater than 20
+    # Warn and threshold extinction coefficients greater than 20 to 20
+    if jnp.any(extinction_coeff_arr > 20):
+        warnings.warn("Extinction coefficient being greater than 20 indicates that the material is almost opaque. "
+                      "In the Transfer Matrix Method, to avoid the coefficients going to 0 and the gradient being zero, "
+                      "extinction coefficients greater than 20 have been thresholded to 20.", UserWarning)
+        extinction_coeff_arr = jnp.where(extinction_coeff_arr > 20, 20, extinction_coeff_arr)
+
+    # Combine the arrays into a single 2D array
+    # Stack arrays as columns into a 2D array for saving
+    data = jnp.column_stack((wavelength_arr, refractive_index_arr, extinction_coeff_arr))
+
+    # Construct the file path
+    # Create a file path for saving the data based on the material name
+    path_csv = os.path.join(nk_data_dir, f'csv/{material_name}.csv')
+    path_npy = os.path.join(nk_data_dir, f'numpy/{material_name}.npy')
+
+    # Save the file with a header
+    # Convert the jax.numpy array to a numpy array for file saving and write to CSV
+    np.savetxt(path_csv, np.asarray(data), delimiter=',', header='wavelength_in_um,n,k', comments='')
+    np.save(path_npy, np.asarray(data))
+
+    # Provide feedback on file creation
+    # Inform the user whether the file was created or recreated successfully
+    print(f"'{os.path.basename(path_csv)}' {'recreated' if os.path.exists(path_csv) else 'created'} successfully.")
+
+
+def visualize_material_properties(material_name = '', logX = False, logY = False, eV = False, savefig = False, save_path = None):
+    # Load the data from the .csv file
+    data = np.array(load_nk_data_csv(material_name))
+    # Unpack the columns: wavelength, refractive index, extinction coefficient
+    wavelength, refractive_index, extinction_coeff = data.T  # wavelength is in um
+    # Custom chart specs
+    if eV:
+        eV_arr = 1239.8/(wavelength*1e3) # E(eV) = 1239.8 / wavelength (nm)
+    # Creating plot for refractive_index
+    fig, ax1 = plt.subplots(figsize=(10, 6))
+    color_n = 'navy'
+    ax1.set_ylabel('Refractive Index (n)', color=color_n, fontsize=14, fontweight='bold')
+    if not eV:
+        ax1.set_xlabel('Wavelength (um)', fontsize=14, fontweight='bold')
+        ax1.plot(wavelength, refractive_index, color=color_n, linewidth=2, label='Refractive Index (n)')
+    else:
+        ax1.set_xlabel('Photon energy (eV)', fontsize=14, fontweight='bold')
+        ax1.plot(eV_arr, refractive_index, color=color_n, linewidth=2, label='Refractive Index (n)')
+    ax1.tick_params(axis='y', labelcolor=color_n, labelsize=12)
+    ax1.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.7)
+    # Creating a second y-axis for the extinction coefficient (k)
+    ax2 = ax1.twinx()
+    color_k = 'crimson'
+    ax2.set_ylabel('Extinction Coefficient (k)', color=color_k, fontsize=14, fontweight='bold')
+    if not eV:
+        ax2.plot(wavelength, extinction_coeff, color=color_k, linewidth=2, linestyle='-', label='Extinction Coefficient (k)')
+    else:
+        ax2.plot(eV_arr, extinction_coeff, color=color_k, linewidth=2, linestyle='-', label='Extinction Coefficient (k)')
+    ax2.tick_params(axis='y', labelcolor=color_k, labelsize=12)
+    if logX:
+        # Set the x-axis to logarithmic scale
+        plt.xscale('log')
+    if logY:
+        # Set the y-axis to logarithmic scale
+        plt.yscale('log')
+    # Adding title
+    plt.title(f'Refractive Index (n) and Extinction Coefficient (k) vs Wavelength for {material_name}', fontsize=16, fontweight='bold', pad=20)
+    fig.tight_layout()
+    # Save the figure as a PNG if savefig True
+    if savefig:
+        # Check that save_path is not an empty string or None
+        if not save_path:
+            raise ValueError("save_path cannot be an empty string or None")
+        # Ensure the save directory exists
+        os.makedirs(save_path, exist_ok=True)
+        # Construct the full save path with filename
+        full_save_path = os.path.join(save_path, f'{material_name}_nk_plot.png')
+        # Save the figure
+        plt.savefig(full_save_path, dpi=300)
+        print(f"Figure saved successfully at: {full_save_path}")
+    plt.show()
+
+def common_wavelength_band(material_list: List[str]) -> Tuple[float, float]:
+    """
+    Compute the common wavelength band across a list of materials based on their n-k data.
+
+    Args:
+    ----------
+    material_list : Optional[List[str]]
+        A list of material names for which the common wavelength band is to be calculated.
+
+    Returns:
+    -------
+    Optional[Tuple[float, float]]
+        A tuple containing the minimum and maximum wavelength of the common band.
+        Returns None if no common wavelength band exists.
+
+    Raises:
+    ------
+    ValueError:
+        If the material_list is empty or None.
+    """
+    if not material_list:
+        raise ValueError("Material list cannot be empty or None.")
+
+    # Initialize wavelength bounds
+    min_wavelength = -jnp.inf
+    max_wavelength = jnp.inf
+
+    # Iterate through each material's wavelength range
+    for material_name in material_list:
+        wavelength_arr = load_nk_data_csv(material_name)[:, 0]
+        material_min, material_max = jnp.min(wavelength_arr), jnp.max(wavelength_arr)
+
+        # Update the min_wavelength and max_wavelength to find the common range
+        min_wavelength = jnp.maximum(min_wavelength, material_min)
+        max_wavelength = jnp.minimum(max_wavelength, material_max)
+
+        # Early exit if no common range is possible
+        if min_wavelength > max_wavelength:
+            return None
+
+    return min_wavelength, max_wavelength
+
+
+def calculate_chromatic_dispersion(material_name: str) -> jnp.ndarray:
+    """
+    Calculate the chromatic dispersion, which is the derivative of the refractive index
+    with respect to wavelength.
+
+    Args:
+        material_name (str): Name of the material.
+
+    Returns:
+        jnp.ndarray: Array containing the chromatic dispersion (d n / d wavelength).
+    """
+    # Fetch the nk data for the material
+    nk_data = load_nk_data_csv(material_name)
+
+    # Unpack the columns: wavelength, refractive index, extinction coefficient
+    wavelength, refractive_index, _ = nk_data.T  # nk_data.T transposes the matrix to easily unpack columns
+
+    # Define a function to compute the refractive index as a function of wavelength
+    def n_func(wl: jnp.ndarray) -> jnp.ndarray:
+        return jnp.interp(wl, wavelength, refractive_index)
+
+    # Compute the derivative of the refractive index function with respect to wavelength
+    dn_dw = vmap(grad(n_func))(wavelength)
+
+    return dn_dw
+
+def get_max_absorption_wavelength(material_name: str) -> float:
+    """
+    Calculate the wavelength at which the absorption coefficient is maximized.
+
+    Args:
+        material_name (str): Name of the material.
+
+    Returns:
+        float: Wavelength (in μm) corresponding to the maximum absorption coefficient.
+    """
+    # Fetch the nk data for the material
+    data = load_nk_data_csv(material_name)
+    # Unpack the columns: wavelength, refractive index (not used), extinction coefficient
+    wavelength, _, k = data.T  # data.T transposes the matrix to easily unpack columns
+    # Calculate the absorption coefficient: α(λ) = 4 * π * k / λ
+    absorption_coefficient = 4 * jnp.pi * k / wavelength
+    # Identify the index of the maximum absorption coefficient
+    max_absorption_index = jnp.argmax(absorption_coefficient)
+
+    # Return the wavelength corresponding to the maximum absorption
+    return float(wavelength[max_absorption_index])
\ No newline at end of file
diff --git a/tmmax/fresnel.py b/tmmax/fresnel.py
new file mode 100644
index 0000000..fa07271
--- /dev/null
+++ b/tmmax/fresnel.py
@@ -0,0 +1,111 @@
+import jax.numpy as jnp  # Import the jax numpy module for numerical and mathematical operations, used for efficient array manipulation and computations on CPUs/GPUs/TPUs.
+from jax import Array  # Import the Array class from jax, which is used for creating arrays in JAX, though it's included here primarily for type specification purpose.
+from jax.typing import ArrayLike  # Import ArrayLike from jax.typing. It is an annotation for any value that is safe to implicitly cast to a JAX array
+
+def fresnel_s(first_layer_n: ArrayLike,
+              second_layer_n: ArrayLike,
+              first_layer_theta: ArrayLike,
+              second_layer_theta: ArrayLike) -> Array:
+    """
+    This function calculates the Fresnel reflection (r_s) and transmission (t_s) coefficients
+    for s-polarized light (electric field perpendicular to the plane of incidence) at the interface
+    between two materials. The inputs are the refractive indices and the angles of incidence and
+    refraction for the two layers.
+
+    Args:
+        first_layer_n (ArrayLike): Refractive index of the first layer (incident medium).
+            This can be a single value or an array if computing for multiple incident angles/materials.
+        second_layer_n (ArrayLike): Refractive index of the second layer (transmitted medium).
+            Similar to the first argument, this can be a single value or an array.
+        first_layer_theta (ArrayLike): Angle of incidence in the first layer (in radians).
+            This can be a single value or an array.
+        second_layer_theta (ArrayLike): Angle of refraction in the second layer (in radians).
+            This can be a single value or an array.
+
+    Returns:
+        Array: A JAX array containing two elements:
+            - r_s: The Fresnel reflection coefficient for s-polarized light.
+            - t_s: The Fresnel transmission coefficient for s-polarized light.
+
+    Function Explanation:
+    This function implements the Fresnel equations specifically for s-polarized light, where the electric
+    field is perpendicular to the plane of incidence. These equations describe how light interacts at the
+    boundary between two different optical media. The reflection coefficient, `r_s`, measures the ratio
+    of reflected to incident electric field amplitude, while the transmission coefficient, `t_s`, measures
+    the ratio of transmitted to incident electric field amplitude. These coefficients depend on the refractive
+    indices of the two media and the angles of incidence and refraction. The function returns a single JAX 
+    array containing both coefficients, enabling compatibility with differentiable programming and GPU/TPU 
+    acceleration.
+    """
+
+    cos_first_theta = jnp.cos(first_layer_theta)  # Calculate the cosine of the incident angle (first layer).
+    cos_second_theta = jnp.cos(second_layer_theta)  # Calculate the cosine of the refraction angle (second layer).
+    
+    # Multiply the refractive index of the first layer by the cosine of its angle.
+    first_ncostheta = jnp.multiply(first_layer_n, cos_first_theta)
+    
+    # Multiply the refractive index of the second layer by the cosine of its angle.
+    second_ncostheta = jnp.multiply(second_layer_n, cos_second_theta)
+    
+    # Compute the sum of the two terms: n1*cos(theta1) + n2*cos(theta2).
+    add_ncosthetas = jnp.add(first_ncostheta, second_ncostheta)
+
+    # Calculate the reflection coefficient (r_s) for s-polarized light.
+    # Formula: r_s = (n1*cos(theta1) - n2*cos(theta2)) / (n1*cos(theta1) + n2*cos(theta2))
+    # This represents the fraction of light that is reflected at the interface.
+    r_s = jnp.true_divide(jnp.subtract(first_ncostheta, second_ncostheta), add_ncosthetas)
+
+    # Calculate the transmission coefficient (t_s) for s-polarized light.
+    # Formula: t_s = 2*n1*cos(theta1) / (n1*cos(theta1) + n2*cos(theta2))
+    # This represents the fraction of light that passes through the interface.
+    t_s = jnp.true_divide(jnp.multiply(2, first_ncostheta), add_ncosthetas)
+
+    # Combine the reflection and transmission coefficients into a single array.
+    # The resulting array contains both coefficients for easy use in further computations.
+    return jnp.stack([r_s, t_s])  # Return a stacked array containing r_s and t_s coefficients.
+
+def fresnel_p(first_layer_n: ArrayLike,
+              second_layer_n: ArrayLike,
+              first_layer_theta: ArrayLike,
+              second_layer_theta: ArrayLike) -> Array:
+    """
+    This function calculates the Fresnel reflection (r_p) and transmission (t_p) coefficients
+    for p-polarized light at the interface between two different media. It uses the refractive indices
+    of the two media (_first_layer_n and _second_layer_n) and the incident and transmitted angles
+    (_first_layer_theta and _second_layer_theta) to compute these values.
+
+    Args:
+        _first_layer_n: ArrayLike Refractive index of the first medium (can be float or ndarray).
+        _second_layer_n: ArrayLike Refractive index of the second medium (can be float or ndarray).
+        _first_layer_theta: ArrayLike Incident angle (in radians) in the first medium (can be float or ndarray).
+        _second_layer_theta: ArrayLike Transmitted angle (in radians) in the second medium (can be float or ndarray).
+
+    Returns:
+        Array: A tuple containing two arrays:
+            - r_p: The reflection coefficient for p-polarized light.
+            - t_p: The transmission coefficient for p-polarized light.
+    """
+    cos_first_theta = jnp.cos(first_layer_theta)  # Calculate the cosine of the incident angle in the first medium
+    cos_second_theta = jnp.cos(second_layer_theta)  # Calculate the cosine of the transmitted angle in the second medium
+
+    second_n_first_costheta = jnp.multiply(second_layer_n, cos_first_theta)  # Multiply the refractive index of the second medium with the cosine of the incident angle
+    first_n_second_costheta = jnp.multiply(first_layer_n, cos_second_theta)  # Multiply the refractive index of the first medium with the cosine of the transmitted angle
+
+    add_ncosthetas = jnp.add(second_n_first_costheta, first_n_second_costheta)  # Add the two previous results together
+
+    # Calculate the reflection coefficient for p-polarized light (r_p)
+    # This equation is based on the Fresnel equations for p-polarization, where
+    # r_p is the ratio of the reflected and incident electric field amplitudes for p-polarized light.
+    r_p = jnp.true_divide(jnp.subtract(second_n_first_costheta, first_n_second_costheta), add_ncosthetas)
+    # Subtract the second result from the first result, divide by the sum calculated earlier to get r_p.
+
+    # Calculate the transmission coefficient for p-polarized light (t_p)
+    # This equation is also derived from the Fresnel equations for p-polarization.
+    # t_p represents the ratio of the transmitted and incident electric field amplitudes.
+    t_p = jnp.true_divide(jnp.multiply(2, jnp.multiply(first_layer_n, cos_first_theta)), add_ncosthetas)
+    # Multiply the refractive index of the first medium with the cosine of the incident angle, then multiply by 2.
+    # Divide the result by the sum calculated earlier to get t_p.
+
+    # Return the reflection and transmission coefficients as a tuple of jnp arrays
+    # Both r_p and t_p are essential for understanding how light interacts with different layers.
+    return jnp.stack([r_p, t_p])  # Stack r_p and t_p into a single array (tuple) and return them
\ No newline at end of file
diff --git a/tmmax/nk_data/csv/ALON.csv b/tmmax/nk_data/csv/ALON.csv
new file mode 100644
index 0000000..69fd62c
--- /dev/null
+++ b/tmmax/nk_data/csv/ALON.csv
@@ -0,0 +1,13 @@
+wavelength_in_um,n,k
+2.399999999999999911e-01,1.913999999999999924e+00,5.961000000000000187e-06
+3.400000000000000244e-01,1.824999999999999956e+00,5.249000000000000481e-07
+4.000000000000000222e-01,1.806999999999999940e+00,4.806000000000000022e-07
+5.999999999999999778e-01,1.778999999999999915e+00,5.490999999999999604e-07
+1.000000000000000000e+00,1.749000000000000110e+00,6.446000000000000413e-07
+2.799999999999999822e+00,1.721999999999999975e+00,1.047000000000000098e-06
+3.399999999999999911e+00,1.705999999999999961e+00,9.469999999999999963e-07
+4.000000000000000000e+00,1.685000000000000053e+00,5.125000000000000059e-06
+4.599999999999999645e+00,1.659999999999999920e+00,2.676000000000000129e-05
+5.000000000000000000e+00,1.653000000000000025e+00,6.271000000000000098e-05
+5.400000000000000355e+00,1.606999999999999984e+00,3.044000000000000265e-04
+5.599999999999999645e+00,1.586000000000000076e+00,3.686000000000000149e-04
diff --git a/tmmax/nk_data/csv/Air.csv b/tmmax/nk_data/csv/Air.csv
new file mode 100644
index 0000000..68b653e
--- /dev/null
+++ b/tmmax/nk_data/csv/Air.csv
@@ -0,0 +1,103 @@
+wavelength_in_um,n,k
+0.2,1.000308003,0
+0.2446,1.000303036,0
+0.2592,1.00029911,0
+0.2738,1.000295938,0
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+0.7556,1.000275346,0
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+0.7848,1.000275144,0
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+0.814,1.000274964,0
+0.8286,1.000274881,0
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+0.887,1.00027459,0
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+0.9308,1.000274407,0
+0.9454,1.000274352,0
+0.96,1.000274299,0
+0.9746,1.000274249,0
+0.9892,1.0002742,0
+1.004,1.000274154,0
+1.018,1.000274111,0
+1.033,1.000274068,0
+1.048,1.000274027,0
+1.062,1.00027399,0
+1.077,1.000273951,0
+1.091,1.000273917,0
+1.106,1.000273882,0
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+1.135,1.000273818,0
+1.15,1.000273787,0
+1.164,1.000273759,0
+1.179,1.00027373,0
+1.194,1.000273702,0
+1.208,1.000273677,0
+1.223,1.000273651,0
+1.237,1.000273627,0
+1.252,1.000273603,0
+1.267,1.00027358,0
+1.281,1.000273559,0
+1.296,1.000273537,0
+1.31,1.000273518,0
+1.325,1.000273497,0
+1.34,1.000273478,0
+1.354,1.00027346,0
+1.369,1.000273442,0
+1.383,1.000273425,0
+1.398,1.000273408,0
+1.413,1.000273391,0
+1.427,1.000273376,0
+1.442,1.00027336,0
+1.456,1.000273346,0
+1.471,1.000273331,0
+1.486,1.000273317,0
+1.5,1.000273304,0
+1.515,1.00027329,0
+1.529,1.000273278,0
+1.544,1.000273265,0
+1.559,1.000273253,0
+1.573,1.000273242,0
+1.588,1.00027323,0
+1.602,1.000273219,0
+1.617,1.000273208,0
+1.632,1.000273197,0
+1.646,1.000273188,0
+1.661,1.000273177,0
+1.675,1.000273168,0
+1.69,1.000273158,0
+10,1.000273158,0
diff --git a/tmmax/nk_data/csv/Al2O3.csv b/tmmax/nk_data/csv/Al2O3.csv
new file mode 100644
index 0000000..eeca6c7
--- /dev/null
+++ b/tmmax/nk_data/csv/Al2O3.csv
@@ -0,0 +1,701 @@
+wavelength_in_um,n,k
+2.110019999999999951e-01,1.785916000000000059e+00,1.309000000000000051e-03
+2.125759999999999872e-01,1.781681000000000070e+00,1.116999999999999937e-03
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+2.204470000000000041e-01,1.763158999999999921e+00,5.220000000000000031e-04
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diff --git a/tmmax/nk_data/csv/CaF2.csv b/tmmax/nk_data/csv/CaF2.csv
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diff --git a/tmmax/nk_data/csv/CdS.csv b/tmmax/nk_data/csv/CdS.csv
new file mode 100644
index 0000000..f1c0986
--- /dev/null
+++ b/tmmax/nk_data/csv/CdS.csv
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diff --git a/tmmax/nk_data/csv/CdTe.csv b/tmmax/nk_data/csv/CdTe.csv
new file mode 100644
index 0000000..b5725ad
--- /dev/null
+++ b/tmmax/nk_data/csv/CdTe.csv
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diff --git a/tmmax/nk_data/csv/CeF3.csv b/tmmax/nk_data/csv/CeF3.csv
new file mode 100644
index 0000000..2209527
--- /dev/null
+++ b/tmmax/nk_data/csv/CeF3.csv
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diff --git a/tmmax/nk_data/csv/Ge.csv b/tmmax/nk_data/csv/Ge.csv
new file mode 100644
index 0000000..6bcea9a
--- /dev/null
+++ b/tmmax/nk_data/csv/Ge.csv
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diff --git a/tmmax/nk_data/csv/HfO2.csv b/tmmax/nk_data/csv/HfO2.csv
new file mode 100644
index 0000000..250c4cc
--- /dev/null
+++ b/tmmax/nk_data/csv/HfO2.csv
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diff --git a/tmmax/nk_data/csv/LaF3.csv b/tmmax/nk_data/csv/LaF3.csv
new file mode 100644
index 0000000..771d446
--- /dev/null
+++ b/tmmax/nk_data/csv/LaF3.csv
@@ -0,0 +1,722 @@
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new file mode 100644
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diff --git a/tmmax/nk_data/csv/MgO.csv b/tmmax/nk_data/csv/MgO.csv
new file mode 100644
index 0000000..6b3b1ba
--- /dev/null
+++ b/tmmax/nk_data/csv/MgO.csv
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diff --git a/tmmax/nk_data/csv/PbF2.csv b/tmmax/nk_data/csv/PbF2.csv
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--- /dev/null
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diff --git a/tmmax/nk_data/csv/Si.csv b/tmmax/nk_data/csv/Si.csv
new file mode 100644
index 0000000..8d3c8ea
--- /dev/null
+++ b/tmmax/nk_data/csv/Si.csv
@@ -0,0 +1,1802 @@
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diff --git a/tmmax/nk_data/csv/Si3N4.csv b/tmmax/nk_data/csv/Si3N4.csv
new file mode 100644
index 0000000..b623232
--- /dev/null
+++ b/tmmax/nk_data/csv/Si3N4.csv
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diff --git a/tmmax/nk_data/csv/SiO.csv b/tmmax/nk_data/csv/SiO.csv
new file mode 100644
index 0000000..16bb848
--- /dev/null
+++ b/tmmax/nk_data/csv/SiO.csv
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diff --git a/tmmax/nk_data/csv/SiO2.csv b/tmmax/nk_data/csv/SiO2.csv
new file mode 100644
index 0000000..e490cea
--- /dev/null
+++ b/tmmax/nk_data/csv/SiO2.csv
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diff --git a/tmmax/nk_data/csv/SrF2.csv b/tmmax/nk_data/csv/SrF2.csv
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new file mode 100644
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--- /dev/null
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diff --git a/tmmax/nk_data/csv/Te.csv b/tmmax/nk_data/csv/Te.csv
new file mode 100644
index 0000000..b2af795
--- /dev/null
+++ b/tmmax/nk_data/csv/Te.csv
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diff --git a/tmmax/nk_data/csv/TiO2.csv b/tmmax/nk_data/csv/TiO2.csv
new file mode 100644
index 0000000..9a03fa3
--- /dev/null
+++ b/tmmax/nk_data/csv/TiO2.csv
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diff --git a/tmmax/nk_data/csv/ZnSe.csv b/tmmax/nk_data/csv/ZnSe.csv
new file mode 100644
index 0000000..1c0aaef
--- /dev/null
+++ b/tmmax/nk_data/csv/ZnSe.csv
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diff --git a/tmmax/nk_data/csv/ZrO2.csv b/tmmax/nk_data/csv/ZrO2.csv
new file mode 100644
index 0000000..d6ffc5b
--- /dev/null
+++ b/tmmax/nk_data/csv/ZrO2.csv
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diff --git a/tmmax/reflect_transmit.py b/tmmax/reflect_transmit.py
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--- /dev/null
+++ b/tmmax/reflect_transmit.py
@@ -0,0 +1,552 @@
+import jax.numpy as jnp  # jax's numpy library we will use for all general mathematical operations
+from jax import vmap  # We will vectorize while obtaining rt, that's why we import it
+from jax import Array  # Type definition for JAX arrays
+from jax.typing import ArrayLike  # JAX type hint for array-like objects (supports numpy, JAX arrays, etc.)
+
+from .fresnel import fresnel_s, fresnel_p  # We will use our custom fresnel_s, fresnel_p functions while calculating r, t
+from .cascaded_matmul import coh_cascaded_matrix_multiplication  # We will use this function while calculating coh layers in incoh film
+
+def calculate_reflectance_from_coeff(r: ArrayLike) -> Array:
+    """ 
+    This function calculates the reflectance given the reflection coefficient 'r'.
+    
+    Arguments:
+    r : ArrayLike
+        - This argument represents the reflection coefficient, which is typically a complex number that describes how much light is 
+          reflected when it encounters a surface or interface. 
+        - It can be a scalar or an array of reflection coefficients for multiple values.
+        - The input can be a list, tuple, or any other object that can be converted to a jax array, hence the use of 'ArrayLike'.
+    
+    Returns:
+    Array
+        - The function returns the reflectance, which is calculated as the square of the absolute value of 'r'.
+        - Reflectance is a measure of the fraction of light that is reflected by a surface, and is a real, non-negative quantity.
+    """
+    return jnp.square(jnp.abs(r))  # Calculates the square of the absolute value of 'r', i.e., reflectance.
+
+def calculate_transmittace_from_coeff_s_pol(t: ArrayLike,
+                                            nk_first_layer_of_slab: ArrayLike,
+                                            angle_first_layer_of_slab: ArrayLike,
+                                            nk_last_layer_of_slab: ArrayLike,
+                                            angle_last_layer_of_slab: ArrayLike) -> Array:
+    """
+    This function calculates the transmittance (T) of light with s-polarization through a slab based on the given 
+    transmission coefficient `t`, refractive indices of the first and last layers of the slab, and their corresponding 
+    angles of incidence or transmission. Physically, this ratio accounts for the matching of impedance 
+    between the layers and the energy conservation in the system. The result is a measure of how much light energy 
+    successfully passes through the slab relative to the initial incident energy.
+
+    Arguments:
+    t: ArrayLike
+        The complex transmission coefficient for s-polarized light. This represents the ratio of the transmitted 
+        electric field to the incident electric field.
+    nk_first_layer_of_slab: ArrayLike
+        The complex refractive index of the first layer of the slab. The refractive index (n + ik) combines the real 
+        part (n) that determines the speed of light in the material and the imaginary part (k) that accounts for 
+        absorption losses.
+    angle_first_layer_of_slab: ArrayLike
+        The angle of incidence of light in the first layer of the slab, expressed in radians.
+    nk_last_layer_of_slab: ArrayLike
+        The complex refractive index of the last layer of the slab, which governs the light's behavior as it exits 
+        the slab.
+    angle_last_layer_of_slab: ArrayLike
+        The angle of refraction or transmission in the last layer of the slab, expressed in radians.
+
+    Returns:
+    T: Array
+        The calculated transmittance (T) for the given slab. Transmittance is the proportion of the incident light 
+        intensity that passes through the slab, and it is calculated using the transmission coefficient and the 
+        refractive indices of the involved layers.
+    """
+    T = jnp.multiply(jnp.square(jnp.abs(t)), jnp.true_divide(jnp.real(jnp.multiply(nk_last_layer_of_slab, jnp.cos(angle_last_layer_of_slab))),
+                                                             jnp.real(jnp.multiply(nk_first_layer_of_slab, jnp.cos(angle_first_layer_of_slab)))))
+    return T
+
+def calculate_transmittace_from_coeff_p_pol(t: ArrayLike,
+                                            nk_first_layer_of_slab: ArrayLike,
+                                            angle_first_layer_of_slab: ArrayLike,
+                                            nk_last_layer_of_slab: ArrayLike,
+                                            angle_last_layer_of_slab: ArrayLike) -> Array:
+    """
+    This function calculates the transmittance (T) for p-polarized light incident on a multilayer slab. 
+    Transmittance is a measure of the fraction of incident light energy that passes through a material or structure. 
+    In this function, we consider p-polarized light, which means the electric field vector is parallel to the plane of incidence.
+    Physically, the transmittance quantifies the portion of light energy that successfully 
+    passes through the slab structure, relative to the energy that initially interacted with it. The ratio calculated 
+    incorporates the effects of refraction and wave impedance matching between the layers.
+
+    **Arguments:**
+    - `t`: A complex array-like object representing the transmission coefficient of p-polarized light. 
+      This coefficient is derived from the scattering matrix formalism or similar optical computations and encapsulates
+      the interaction of light with the layers of the slab.
+    - `nk_first_layer_of_slab`: A complex array-like object representing the refractive index of the first layer of the slab. 
+      The refractive index is a fundamental optical property that characterizes how light propagates through the medium.
+    - `angle_first_layer_of_slab`: A real array-like object representing the angle of light incidence (or refraction) 
+      in the first layer of the slab, in radians.
+    - `nk_last_layer_of_slab`: A complex array-like object representing the refractive index of the last layer of the slab. 
+      Similar to the first layer, this describes the propagation of light at the exit side of the slab.
+    - `angle_last_layer_of_slab`: A real array-like object representing the angle of refraction in the last layer of the slab, in radians.
+
+    **Returns:**
+    - `T`: A real-valued array representing the transmittance (fraction of energy transmitted through the slab). 
+      It is calculated using the ratio of energy flux in the outgoing medium to the energy flux in the incoming medium, 
+      taking into account the transmission coefficient (`t`) and the refractive indices (`nk`) and angles (`angle`) 
+      in the first and last layers.
+    """
+    T = jnp.multiply(jnp.square(jnp.abs(t)), jnp.true_divide(jnp.real(jnp.multiply(nk_last_layer_of_slab, jnp.conj(jnp.cos(angle_last_layer_of_slab)))),
+                                                             jnp.real(jnp.multiply(nk_first_layer_of_slab, jnp.conj(jnp.cos(angle_first_layer_of_slab))))))
+    return T
+
+def calculate_transmittace_from_coeff(t: ArrayLike,
+                                      nk_first_layer_of_slab: ArrayLike,
+                                      angle_first_layer_of_slab: ArrayLike,
+                                      nk_last_layer_of_slab: ArrayLike,
+                                      angle_last_layer_of_slab: ArrayLike,
+                                      polarization: ArrayLike) -> Array:
+    """
+    This function calculates the transmittance (T) of a system based on input parameters that describe the transmission coefficient (t), 
+    the optical properties of the first and last layers of a slab, and the polarization of the incident light.
+    This function acts as a selector for two sub-functions: `calculate_transmittace_from_coeff_s_pol` and 
+    `calculate_transmittace_from_coeff_p_pol`. Based on the input `polarization`, it decides whether to calculate 
+    the transmittance for 's' or 'p' polarization. The transmittance quantifies how much light is transmitted 
+    through the slab system and depends on the optical properties and angles at the interfaces.
+
+    Arguments:
+    t: ArrayLike
+        The transmission coefficient of the system, typically a complex value representing amplitude and phase information.
+
+    nk_first_layer_of_slab: ArrayLike
+        The complex refractive index of the first layer of the slab, which determines how light propagates through the layer. 
+        It is a combination of the refractive index (n) and extinction coefficient (k) and may vary with wavelength.
+
+    angle_first_layer_of_slab: ArrayLike
+        The angle of incidence of the light on the first layer of the slab, typically given in radians. 
+        This angle impacts how light refracts or reflects at the interface.
+
+    nk_last_layer_of_slab: ArrayLike
+        The complex refractive index of the last layer of the slab, which describes how the light exits the system.
+
+    angle_last_layer_of_slab: ArrayLike
+        The angle of transmission or refraction through the last layer of the slab. This is critical in determining the exit path of the light.
+
+    polarization: bool
+        A boolean indicating the polarization state of the light:
+        - `False`: Indicates 's' polarization (electric field perpendicular to the plane of incidence).
+        - `True`: Indicates 'p' polarization (electric field parallel to the plane of incidence).
+
+    Returns:
+    Array
+        The transmittance (T) of the system, calculated based on the input polarization. The result is dependent on whether 
+        the polarization is 's' or 'p', and the function redirects to the appropriate sub-function to perform the computation.
+    """
+    # Use `jnp.select` to determine which transmittance calculation to perform based on the polarization value.
+    return jnp.select(condlist=[jnp.array_equal(polarization, jnp.array([False], dtype=bool)),
+                                jnp.array_equal(polarization, jnp.array([True], dtype=bool))],
+                    choicelist=[calculate_transmittace_from_coeff_s_pol(t,
+                                                                        nk_first_layer_of_slab,
+                                                                        angle_first_layer_of_slab,
+                                                                        nk_last_layer_of_slab,
+                                                                        angle_last_layer_of_slab),
+                                calculate_transmittace_from_coeff_p_pol(t,
+                                                                        nk_first_layer_of_slab,
+                                                                        angle_first_layer_of_slab,
+                                                                        nk_last_layer_of_slab,
+                                                                        angle_last_layer_of_slab)])
+
+# THE FUNCTIONS FOR OBTAINING THE COEFFICIENTS:
+
+def compute_rt_at_interface_s(layer_idx: ArrayLike,
+                              nk_angles_stack: ArrayLike) -> Array:
+    """
+    This function computes the reflection (r) and transmission (t) coefficients 
+    for s-polarized light at the interface between two adjacent layers in a stack 
+    of materials. 
+
+    Arguments:
+    - layer_idx: ArrayLike
+        The index of the current layer in the stack. This is used to determine 
+        which layers' refractive indices (n) and angles of incidence (theta) 
+        are used in the calculation. Typically, this is an integer value that 
+        corresponds to the position of the current layer in the material stack.
+
+    - nk_angles_stack: ArrayLike
+        A 2D array of shape [N, 2], where N is the number of layers in the stack. 
+        Each row corresponds to a layer and contains:
+          - [i, 0]: The refractive index (n) of the i-th layer.
+          - [i, 1]: The angle of incidence (theta) of light in the i-th layer, 
+                    measured with respect to the normal of the layer.
+
+    Returns:
+    - rt: Array
+        An array containing the reflection (r) and transmission (t) coefficients 
+        for s-polarized light at the interface between the specified layer (layer_idx) 
+        and the subsequent layer (layer_idx + 1). These coefficients describe the 
+        behavior of light as it transitions between the two layers.
+    """
+    # Extract the refractive index (n) of the first layer from nk_angles_stack at the specified layer index
+    first_layer_n = nk_angles_stack.at[layer_idx, 0].get()
+    
+    # Extract the refractive index (n) of the second layer (layer_idx + 1) from nk_angles_stack
+    second_layer_n = nk_angles_stack.at[jnp.add(layer_idx, jnp.array([1], dtype=jnp.int32)), 0].get()
+    
+    # Extract the angle of incidence (theta) of the first layer from nk_angles_stack at the specified layer index
+    first_layer_theta = nk_angles_stack.at[layer_idx, 1].get()
+    
+    # Extract the angle of incidence (theta) of the second layer (layer_idx + 1) from nk_angles_stack
+    second_layer_theta = nk_angles_stack.at[jnp.add(layer_idx, jnp.array([1], dtype=jnp.int32)), 1].get()
+    
+    # Compute the reflection and transmission coefficients for s-polarized light
+    # between the two layers using the Fresnel equations.
+    rt = fresnel_s(
+        first_layer_n=first_layer_n,
+        second_layer_n=second_layer_n,
+        first_layer_theta=first_layer_theta,
+        second_layer_theta=second_layer_theta
+    )
+    
+    # Return the computed reflection and transmission coefficients as an array
+    return rt
+
+def compute_rt_at_interface_p(layer_idx: ArrayLike, 
+                              nk_angles_stack: ArrayLike) -> Array:
+    """
+    This function calculates the reflection and transmission coefficients for p-polarized light at the 
+    interface between two adjacent layers of different materials in a multilayer optical system. 
+    The function calls `fresnel_p`, a helper function that uses the Fresnel equations for p-polarized light to 
+    determine the reflection and transmission coefficients. These coefficients are crucial for modeling optical
+    behavior in multilayer systems.
+
+    Arguments:
+    - layer_idx: ArrayLike
+        The index of the current layer. It is used to access the optical properties (refractive index `n`) 
+        and angles (relative to the layer normal) of the current layer and the adjacent layer.
+        For example, if `layer_idx` is 2, the function retrieves the refractive indices and angles for 
+        layer 2 and layer 3.
+
+    - nk_angles_stack: ArrayLike
+        A stack (2D array) containing the refractive indices (`n`) and angles for all layers. 
+        The shape of this array is `[N, 2]`, where `N` is the number of layers.
+        - `nk_angles_stack[i, 0]` provides the refractive index `n` for the i-th layer.
+        - `nk_angles_stack[i, 1]` provides the angle of incidence (or refraction) of light 
+          relative to the normal for the i-th layer.
+
+    Returns:
+    - Array
+        The reflection and transmission coefficients for p-polarized light at the interface between the `layer_idx`-th layer and the `layer_idx + 1`-th layer. 
+        These coefficients are computed using the Fresnel equations for p-polarized light.
+    """
+    
+    # Retrieve the refractive index `n` of the first layer from `nk_angles_stack` using the given `layer_idx`.
+    first_layer_n = nk_angles_stack.at[layer_idx, 0].get()
+    
+    # Retrieve the refractive index `n` of the second layer (next layer) by adding 1 to `layer_idx` and accessing `nk_angles_stack`.
+    second_layer_n = nk_angles_stack.at[jnp.add(layer_idx, jnp.array([1], dtype=jnp.int32)), 0].get()
+    
+    # Retrieve the angle `theta` (relative to the normal) of the first layer from `nk_angles_stack` using `layer_idx`.
+    first_layer_theta = nk_angles_stack.at[layer_idx, 1].get()
+    
+    # Retrieve the angle `theta` (relative to the normal) of the second layer (next layer) by adding 1 to `layer_idx` and accessing `nk_angles_stack`.
+    second_layer_theta = nk_angles_stack.at[jnp.add(layer_idx, jnp.array([1], dtype=jnp.int32)), 1].get()
+    
+    # Compute the reflection and transmission coefficients for p-polarized light using the `fresnel_p` function.
+    # The function is called with the refractive indices and angles of the first and second layers.
+    rt = fresnel_p(first_layer_n=first_layer_n,
+                   second_layer_n=second_layer_n,
+                   first_layer_theta=first_layer_theta,
+                   second_layer_theta=second_layer_theta)
+    
+    # Return the computed reflection and transmission coefficients.
+    return rt
+
+def vectorized_rt_s_pol():
+    """
+    The purpose of this function is to improve computational efficiency by vectorizing 
+    the reflection and transmission coefficient calculations for s-polarized light. By 
+    using JAX's `vmap`, the computations can process batched `index` values in mapped 
+    way while ensuring that `nk_angles_stack` remains fixed.
+
+    Arguments:
+        None: This function does not take any direct input arguments. However, the returned 
+              vectorized function expects the following arguments:
+              - `index` (the first argument): A batched array representing the indices of the 
+                nk_angles_stack array which is used for computation of  reflection and 
+                transmission coefficients. This argument is vectorized along its first axis.
+              - `nk_angles_stack` (the second argument): A non-vectorized array containing 
+                the optical properties and angles of incidence that remain the same across all
+                computations.
+
+    Returns:
+        Callable: A vectorized version of the `compute_rt_at_interface_s` function. This 
+                  function applies the computations element-wise over the first axis of the 
+                  `index` argument while keeping the `nk_angles_stack` argument constant.
+    """
+    return vmap(compute_rt_at_interface_s, (0, None))
+
+def vectorized_rt_p_pol():
+    """
+    The purpose of this function is to improve computational efficiency by vectorizing 
+    the reflection and transmission coefficient calculations for p-polarized light. By 
+    using JAX's `vmap`, the computations can process batched `index` values in mapped 
+    way while ensuring that `nk_angles_stack` remains fixed.
+
+    Arguments:
+        None: This function does not take any direct input arguments. However, the returned 
+              vectorized function expects the following arguments:
+              - `index` (the first argument): A batched array representing the indices of the 
+                nk_angles_stack array which is used for computation of  reflection and 
+                transmission coefficients. This argument is vectorized along its first axis.
+              - `nk_angles_stack` (the second argument): A non-vectorized array containing 
+                the optical properties and angles of incidence that remain the same across all
+                computations.
+
+    Returns:
+        Callable: A vectorized version of the `compute_rt_at_interface_p` function. This 
+                  function applies the computations element-wise over the first axis of the 
+                  `index` argument while keeping the `nk_angles_stack` argument constant.
+    """
+    return vmap(compute_rt_at_interface_p, (0, None))
+
+def polarization_based_rt_selection(layer_indices: ArrayLike, 
+                                    nk_angles_stack: ArrayLike, 
+                                    polarization: ArrayLike) -> Array:
+    """ 
+    This function selects the appropriate vectorized reflection and transmission calculation function 
+    based on the polarization type (s or p-polarization).
+
+    Arguments:
+    layer_indices: 
+        - This is an input array of indices specifying the layers in a multilayer thin film. It is an 
+          integer array (of type int32) that indicates the specific layers over which the r and t 
+          calculations are to be performed.
+        
+    nk_angles_stack:
+        - This is an array containing the refractive index (n) and extinction coefficient (k) values 
+          for each layer. It also includes the angle between the layer normal and the light ray for 
+          each layer. This stack is used in the RT calculations to determine how the light propagates
+          through each layer.
+        
+    polarization:
+        - This is a boolean array, which dictates the type of polarization for the calculation:
+            - False indicates s-polarization (perpendicular polarization).
+            - True indicates p-polarization (parallel polarization).
+
+    Returns:
+        Array: This function returns an array of reflection and transmission coefficients calculated using 
+            either the s-polarization or p-polarization. Depending on the polarization input, 
+            it calls different vectorized rt functions.
+    """
+
+    return jnp.select(condlist=[jnp.array_equal(polarization, jnp.array([False], dtype=bool)),
+                                jnp.array_equal(polarization, jnp.array([True], dtype=bool))],
+                    choicelist=[vectorized_rt_s_pol()(layer_indices, nk_angles_stack),
+                                vectorized_rt_p_pol()(layer_indices, nk_angles_stack)])
+
+def compute_rt(nk_list: ArrayLike, angles: ArrayLike, polarization: ArrayLike) -> Array:
+    """
+    This function computes the reflectance (rt) for a multilayer structure. 
+    It takes in three arguments:
+    
+    - nk_list: This is a list (or array) containing the complex refractive index values for the layers in the multilayer stack. 
+              The complex refractive index is typically represented as `n + ik`, where `n` is the real refractive index and `k` 
+              is the extinction coefficient. The list `nk_list` contains the refractive index values for all N+2 media in the 
+              stack structure, including the two outermost media (such as air and substrate).
+    
+    - angles: This array contains the incident angles (the angle between the normal to the surface and the incoming light) for
+              each medium in the multilayer stack. The angles correspond to the light's propagation direction in each layer of 
+              the stack.
+    
+    - polarization: This is a boolean flag (True or False). If `polarization` is False, the function will use the s-polarization (perpendicular polarization) in calculations, 
+                    and if `True`, it will use the p-polarization (parallel polarization). This flag determines how the reflectance (rt) is computed 
+                    based on the light's polarization state.
+    """
+    
+    # Concatenate the complex refractive indices and angles into a stack (a 2D array).
+    # `nk_list` is reshaped to add an extra dimension (axis 1) and then concatenated with `angles`, 
+    # creating a 2D stack of both `nk` values (refractive index and extinction coefficient) and angles.
+    nk_angles_stack = jnp.concat([jnp.expand_dims(nk_list, 1), jnp.expand_dims(angles, 1)], axis=1)
+    
+    # Compute the stop value, which is the size of `nk_list` minus 1, to define the last layer in the multilayer stack.
+    stop_value = int(jnp.size(nk_list)) - 1
+    
+    # Generate a 1D array `layer_indices` that contains the indices of the layers, from 0 to `stop_value-1`.
+    # This is used for iterating over each layer in the multilayer stack structure.
+    layer_indices = jnp.arange(stop_value, dtype=jnp.int32)
+
+    # Call another function `polarization_based_rt_selection` with the `layer_indices`, `nk_angles_stack`, and `polarization`
+    # to compute the reflectance (rt). This function likely takes care of the specific calculations based on polarization 
+    # and uses the layer information to compute the reflectance at each interface in the stack.
+    return polarization_based_rt_selection(layer_indices, nk_angles_stack, polarization)
+
+def compute_r_t_magnitudes_incoh(coherency_index: ArrayLike, rts: ArrayLike, nk_list: ArrayLike, layer_angles: ArrayLike, polarization: ArrayLike) -> Array:
+    R = calculate_reflectance_from_coeff(rts.at[coherency_index,0].get())
+    T = calculate_transmittace_from_coeff(t = rts.at[coherency_index,1].get(),
+                                          nk_first_layer_of_slab = nk_list.at[coherency_index].get(),
+                                          angle_first_layer_of_slab = layer_angles.at[coherency_index].get(),
+                                          nk_last_layer_of_slab = nk_list.at[coherency_index+1].get(),
+                                          angle_last_layer_of_slab = layer_angles.at[coherency_index+1].get(),
+                                          polarization = polarization)
+    return R,T
+
+def compute_incoh_r_t_magnitudes_incoh_film(coherency_index: ArrayLike, rts: ArrayLike, nk_list: ArrayLike, layer_angles: ArrayLike, polarization: ArrayLike):
+    """
+    This function calculates the reflection and transmission magnitudes for an incoherent layer in the multilayer optical thin films.
+
+    Purpose:
+    It computes the reflection (R) and transmission (T) magnitudes of an incoherent multilayer optical thin film 
+    using precomputed reflection and transmission coefficients (`rts`), along with refractive indices (`nk_list`), 
+    layer angles (`layer_angles`), and polarization states.
+
+    Arguments:
+    - coherency_index (ArrayLike): Index representing the specific layer in the incoherent multilayer structure.
+    - rts (ArrayLike): An array containing reflection and transmission coefficients for different layers.
+    - nk_list (ArrayLike): An array containing the complex refractive indices of the layers.
+    - layer_angles (ArrayLike): An array containing the angles of incidence or refraction for each layer.
+    - polarization (ArrayLike): An array indicating the polarization state of the light (s- or p-polarized).
+
+    Returns:
+    - R,T (Tuple): A tuple containing the calculated reflection magnitude (R) and transmission magnitude (T).
+    """
+
+    # Extract and compute the reflection magnitude from the reflection coefficient
+    R = calculate_reflectance_from_coeff(rts.at[coherency_index, 0].get())
+
+    # Compute the transmission magnitude using the transmission coefficient 
+    # and the refractive indices and angles of the first and last layers in the slab.
+    T = calculate_transmittace_from_coeff(
+        t = rts.at[coherency_index, 1].get(),  # Transmission coefficient for the current layer
+        nk_first_layer_of_slab = nk_list.at[coherency_index].get(),  # Refractive index of the first layer
+        angle_first_layer_of_slab = layer_angles.at[coherency_index].get(),  # Angle of the first layer
+        nk_last_layer_of_slab = nk_list.at[coherency_index + 1].get(),  # Refractive index of the last layer
+        angle_last_layer_of_slab = layer_angles.at[coherency_index + 1].get(),  # Angle of the last layer
+        polarization = polarization  # Polarization state of the light 0 or 1 represents 's' or 'p'
+    )
+
+    # Return calculated reflection and transmission magnitudes
+    return R, T
+
+def compute_coh_r_t_magnitudes_incoh_film(coherency_index: ArrayLike, coherent_layer_indices: ArrayLike, rts: ArrayLike, 
+                                          layer_phases: ArrayLike, nk_list: ArrayLike, layer_angles: ArrayLike, polarization: ArrayLike):
+    """
+    Computes the reflection (R) and transmission (T) magnitudes of the coherent layers 
+    in an incoherent multilayer optical thin film. The coherent layer can consist of 
+    one or more adjacent coherent stacks.
+    
+    Arguments:
+        coherency_index (ArrayLike): Index representing the position of the coherent stack within the multilayer film.
+        coherent_layer_indices (ArrayLike): A 2D array where each row defines the start and end indices 
+                                           of a coherent stack in the multilayer film.
+        rts (ArrayLike): A 2D array containing reflection and transmission coefficients for all layers.
+        layer_phases (ArrayLike): Phase shifts corresponding to each layer in the multilayer system.
+        nk_list (ArrayLike): A list of complex refractive indices for each layer.
+        layer_angles (ArrayLike): A list of angles of incidence/refraction for each layer.
+        polarization (ArrayLike): Polarization state of the incident light (TE or TM mode).
+    
+    Returns:
+        Tuple (R, T):
+            R (float): Reflectance of the coherent stack.
+            T (float): Transmittance of the coherent stack.
+    """
+    # Determine the first and last indices of the coherent stack within the multilayer system
+    start_index = coherent_layer_indices.at[coherency_index-1, 0].get() - 1
+    last_index = coherent_layer_indices.at[coherency_index-1, 1].get() + 1
+    
+    # Extract the reflection and transmission coefficients for the coherent stack
+    stack_rts = rts.at[start_index:last_index, :].get()
+    
+    # Extract the phase shifts corresponding to each layer in the coherent stack
+    stack_layer_phases = layer_phases.at[start_index:last_index-1].get()
+    
+    # Perform matrix multiplication to obtain the cascaded transfer matrix for the coherent stack
+    tr_matrix = coh_cascaded_matrix_multiplication(phases=stack_layer_phases, rts=stack_rts.at[1:,:].get())
+    
+    # Multiply the initial transmission matrix with the cascaded transfer matrix
+    tr_matrix = jnp.multiply(jnp.true_divide(1, stack_rts.at[0,1].get()), 
+                              jnp.dot(jnp.array([[1, stack_rts.at[0,0].get()], 
+                                                 [stack_rts.at[0,0].get(), 1]]), 
+                                     tr_matrix))
+    
+    # Compute the reflection coefficient
+    r = jnp.true_divide(tr_matrix.at[1,0].get(), tr_matrix.at[0,0].get())
+    
+    # Compute the transmission coefficient
+    t = jnp.true_divide(1, tr_matrix.at[0,0].get())
+    
+    # Compute the reflectance from the reflection coefficient
+    R = calculate_reflectance_from_coeff(r)
+    
+    # Compute the transmittance from the transmission coefficient, considering material properties and polarization
+    T = calculate_transmittace_from_coeff(t,
+                                          nk_list.at[start_index].get(),
+                                          layer_angles.at[start_index].get(),
+                                          nk_list.at[last_index].get(),
+                                          layer_angles.at[last_index].get(),
+                                          polarization)
+    
+    return R, T
+
+def compute_r_t_magnitudes_incoh(coherent_layer_indices: ArrayLike, coherency_indices: ArrayLike, rts: ArrayLike, 
+                                 layer_phases: ArrayLike, nk_list: ArrayLike, layer_angles: ArrayLike, polarization: ArrayLike) -> Array:
+    layer_magnitudes = jnp.zeros((len(coherency_indices)-1, 2))
+
+    for i in range(len(coherency_indices)-1):
+        coherency_index = coherency_indices.at[i].get()
+        condition = jnp.greater(coherency_index, -1)
+        coherency_index = jnp.abs(coherency_index)
+        if condition:
+            R,T = compute_coh_r_t_magnitudes_incoh_film(coherency_index, rts, nk_list, layer_angles, polarization)
+        else:
+            R,T = compute_coh_r_t_magnitudes_incoh_film(coherency_index, coherent_layer_indices, rts, layer_phases, nk_list, layer_angles, polarization)
+
+        layer_magnitudes = layer_magnitudes.at[i,0].set(R)
+        layer_magnitudes = layer_magnitudes.at[i,1].set(T)
+
+    return layer_magnitudes
+
+def compute_r_t_magnitudes_incoh(coherent_layer_indices: ArrayLike, coherency_indices: ArrayLike, rts: ArrayLike, 
+                                 layer_phases: ArrayLike, nk_list: ArrayLike, layer_angles: ArrayLike, polarization: ArrayLike) -> Array:
+    """
+    This function computes the reflection (R) and transmission (T) magnitudes for each layer in an incoherent multilayer optical thin film. 
+    It iterates through the layers of a multilayer optical thin film and calculates the reflection and transmission magnitudes for both coherent 
+    and incoherent layers and stacks.
+
+    Arguments:
+    - coherent_layer_indices (ArrayLike): Indices of the coherent layers in the multilayer system.
+    - coherency_indices (ArrayLike): Indices indicating whether a layer is coherent or incoherent.
+    - rts (ArrayLike): Reflection and transmission coefficients for the layers.
+    - layer_phases (ArrayLike): Phase shifts of the layers due to optical thickness.
+    - nk_list (ArrayLike): Refractive index (n + jk) values of the materials in the multilayer system.
+    - layer_angles (ArrayLike): Incident angles for each layer.
+    - polarization (ArrayLike): Polarization states of the incident light.
+
+    Returns:
+    - layer_magnitudes (Array): A 2D array where each row contains the reflection (R) and transmission (T) magnitudes for a layer.
+    """
+
+    # Initialize an array to store reflection (R) and transmission (T) magnitudes for each layer
+    layer_magnitudes = jnp.zeros((len(coherency_indices) - 1, 2))
+
+    # Iterate over all layers except the last one
+    for i in range(len(coherency_indices) - 1):
+        # Get the coherency index for the current layer
+        coherency_index = coherency_indices.at[i].get()
+
+        # Check if the layer is coherent (coherency_index > -1)
+        condition = jnp.greater(coherency_index, -1)
+
+        # Convert the coherency index to an absolute value
+        coherency_index = jnp.abs(coherency_index)
+
+        if condition:
+            # If the layer is coherent, compute R and T using the appropriate function
+            R, T = compute_coh_r_t_magnitudes_incoh_film(coherency_index, rts, nk_list, layer_angles, polarization)
+        else:
+            # If the layer is incoherent, use an alternative computation including phase information
+            R, T = compute_coh_r_t_magnitudes_incoh_film(coherency_index, coherent_layer_indices, rts, layer_phases, nk_list, layer_angles, polarization)
+
+        # Store the computed reflection and transmission magnitudes in the result array
+        layer_magnitudes = layer_magnitudes.at[i, 0].set(R)
+        layer_magnitudes = layer_magnitudes.at[i, 1].set(T)
+
+    # Return the computed reflection and transmission magnitudes for all layers
+    return layer_magnitudes
+
diff --git a/tmmax/tmm.py b/tmmax/tmm.py
new file mode 100644
index 0000000..0f20a3e
--- /dev/null
+++ b/tmmax/tmm.py
@@ -0,0 +1,706 @@
+import jax.numpy as jnp # jax's numpy library we will use for all general mathematical operations
+from jax import jit, vmap # Importing jit for JIT compilation and vmap for efficient vectorization
+from jax import Array # Type definition for JAX arrays
+from jax.typing import ArrayLike # JAX type hint for array-like objects (supports numpy, JAX arrays, etc.)
+from typing import List, Tuple, Text
+
+from .angle import compute_layer_angles
+from .wavevector import compute_kz, compute_inc_layer_pass
+from .cascaded_matmul import coh_cascaded_matrix_multiplication, incoh_cascaded_matrix_multiplication, compute_first_layer_matrix_coherent, compute_first_layer_matrix_incoherent
+from .data import create_nk_list, material_distribution_to_set, create_data
+from .reflect_transmit import compute_rt, calculate_reflectance_from_coeff, calculate_transmittace_from_coeff, compute_r_t_magnitudes_incoh
+
+def find_coh_and_incoh_indices(coherency_list: ArrayLike):
+    """
+    This function determines the coherent and incoherent layers in an optical multilayer thin-film stack.
+    It analyzes the `coherency_list` to identify consecutive coherent layers and incoherent layers,
+    returning structured information about these layers for further processing in optical simulations.
+
+    Arguments:
+        coherency_list (ArrayLike): An array of integers (dtype=int32) representing the coherency states of the layers.
+                                    The array has N elements for a multilayer stack with N layers, where:
+                                    - 0 indicates a coherent layer.
+                                    - 1 indicates an incoherent layer.
+
+    Returns:
+        1. coherent_groups (Array): A 2D array where each row specifies the start and end indices of consecutive coherent layers.
+        2. incoherent_indices (Array): A 1D array containing indices of incoherent layers.
+        3. coherency_indices (Array): A 1D array containing all coherent groups and incoherent layers, where:
+            - Positive indices correspond to incoherent layers.
+            - Negative indices correspond to coherent stacks, and their (absolute values - 1) indicate the index of the stack in coherent_groups.
+
+    Example:
+        Input: coherency_list = [0, 0, 0, 1, 0, 0, 1] for the multilayer thin film  [ Air | TiO2, SiO2, TiO2, Al2O3, SiO2, TiO2, SiO2 | Air ]
+        Output: 
+            coherent_groups = [[1, 3], [5, 6]]
+            incoherent_indices = [0, 4, 7, 8]
+            coherency_indices = [0, -1, 4, -2, 7, 8]
+    """
+
+    # Add padding to the coherency list with a layer of 1 on both sides for incoming and outgoing incoherent mediums
+    padded_coherency_list = jnp.pad(coherency_list, pad_width=(1, 1), constant_values=1)
+
+    # Compute the binary changes in the padded list to find transitions between 0 and 1
+    changes = jnp.diff(padded_coherency_list, prepend=1 - padded_coherency_list[0])
+
+    # Find the start indices of coherent groups (where changes equal -1)
+    start_indices = jnp.where(changes == -1)[0]
+
+    # Find the end indices of coherent groups (where changes equal 1, adjusted by -1)
+    end_indices = jnp.where(changes == 1)[0] - 1
+
+    # Remove the first element of end_indices to handle padding adjustments
+    end_indices = jnp.delete(end_indices, 0)
+
+    # Combine start and end indices to form coherent groups
+    coherent_groups = jnp.stack([start_indices, end_indices], axis=1)
+
+    # Identify indices of incoherent layers (where value is 1 in the padded list)
+    incoherent_indices = jnp.where(padded_coherency_list == 1)[0]
+
+    # Initialize coherency_indices with all incoherent layer indices
+    coherency_indices = jnp.where(padded_coherency_list == 1)[0]
+
+    # Insert coherent group leading indices into coherency_indices
+    for idx, group in enumerate(coherent_groups):
+        # Get the starting index of the current coherent group
+        leading_index = group[0]
+
+        # Find the position to insert the negative index in coherency_indices
+        insertion_idx = jnp.searchsorted(coherency_indices, leading_index)
+
+        # Insert the negative index corresponding to the current coherent group
+        coherency_indices = jnp.insert(coherency_indices, insertion_idx, -idx - 1)
+
+        # Remove the duplicated entry after insertion (adjusted by insertion_idx)
+        coherency_indices = jnp.delete(coherency_indices, insertion_idx - 1)
+
+    # Return the coherent groups, incoherent indices, and combined coherency indices
+    return coherent_groups, incoherent_indices, coherency_indices
+
+def tmm_coh_single_wl_angle_point(data: ArrayLike,
+                                  material_distribution: ArrayLike,
+                                  thickness_list: ArrayLike,
+                                  wavelength: ArrayLike,
+                                  angle_of_incidence: ArrayLike,
+                                  polarization: ArrayLike) -> Array:
+    """
+    This function implements the Transfer Matrix Method (TMM) to compute the reflectance (R) 
+    and transmittance (T) of a coherent multilayer thin-film structure for a given wavelength, 
+    angle of incidence, and polarization.
+
+    Arguments:
+    ----------
+    data: ArrayLike
+        A 2D array where each row corresponds to the refractive index (n) and extinction 
+        coefficient (k) data of a specific material. The first axis length is the number of 
+        materials, and the second axis is the length of the nk vs wavelength data for each 
+        material.
+
+    material_distribution: ArrayLike
+        An array of integers specifying the material index for each layer in the multilayer 
+        structure, including the incoming and outgoing medium. Its length is N + 2, where 
+        N is the number of thin-film layers.
+
+    thickness_list: ArrayLike
+        A 1D array of floats representing the physical thickness of each thin-film layer. 
+        Its length is N, corresponding to the N layers in the multilayer structure.
+
+    wavelength: ArrayLike
+        The wavelength of the light wave (in the same units as the nk data). This is used 
+        to calculate the optical properties for the specific wavelength.
+
+    angle_of_incidence: ArrayLike
+        The angle (in radians) of the incoming light with respect to the normal of the 
+        multilayer thin-film surface.
+
+    polarization: ArrayLike
+        Specifies the polarization state of the light wave. If `False`, the light is 
+        s-polarized (E field is perpendicular to the plane of incidence), and if `True`, 
+        it is p-polarized (E field is parallel to the plane of incidence).
+
+    Returns:
+    --------
+    Tuple of two scalars:
+        R: Reflectance (a value between 0 and 1, representing the ratio of reflected power 
+           to incident power).
+        T: Transmittance (a value between 0 and 1, representing the ratio of transmitted 
+           power to incident power).
+    """
+
+    # Create a list of refractive indices and extinction coefficients for each layer 
+    # at the given wavelength using the provided material distribution.
+    nk_list = create_nk_list(material_distribution, data, wavelength)
+    
+    # Compute the angles of light propagation within each layer based on Snell's Law
+    # and the input angle of incidence.
+    layer_angles = compute_layer_angles(angle_of_incidence, nk_list, polarization)
+    
+    # Compute the z-component of the wavevector (kz) in each layer, which is wavelength-dependent.
+    kz = compute_kz(nk_list, layer_angles, wavelength)
+    
+    # Compute the phase change in each layer due to the thickness of the layer.
+    # Excludes the incoming and outgoing media (index 0 and -1).
+    layer_phases = jnp.multiply(kz.at[1:-1].get(), thickness_list)
+
+    # Calculate reflection and transmission coefficients at each layer interface.
+    # Excludes the incoming and outgoing media.
+    rt = jnp.squeeze(compute_rt(nk_list = nk_list, angles = layer_angles, polarization = polarization))
+
+    # Multiply transfer matrices for all internal layers to compute the combined matrix.
+    # This represents the cumulative effect of reflections and transmissions.
+    tr_matrix = coh_cascaded_matrix_multiplication(phases = layer_phases, rts = rt.at[1:,:].get())
+
+    # Incorporate the reflection and transmission coefficients of the incoming medium
+    # into the total transfer matrix.
+    tr_matrix = jnp.multiply(jnp.true_divide(1, rt.at[0,1].get()), jnp.dot(jnp.array([[1, rt.at[0,0].get()], [rt.at[0,0].get(), 1]]), tr_matrix))
+
+    # Compute the first layer matrix for the incoming medium
+    first_layer_matrix = compute_first_layer_matrix_coherent(r = rt.at[0,0].get(),
+                                                             t = rt.at[0,1].get())
+    # Multiply the first layer matrix with the transfer matrix
+    tr_matrix = jnp.matmul(first_layer_matrix, tr_matrix)
+
+    # Extract the complex reflectance coefficient (r) from the total transfer matrix.
+    r = jnp.true_divide(tr_matrix.at[1,0].get(), tr_matrix.at[0,0].get())
+
+    # Extract the complex transmittance coefficient (t) from the total transfer matrix.
+    t = jnp.true_divide(1, tr_matrix.at[0,0].get())
+
+    # Compute the reflectance (R) from the reflectance coefficient.
+    R = calculate_reflectance_from_coeff(r)
+
+    # Compute the transmittance (T) from the transmittance coefficient, accounting for
+    # impedance matching and angle-dependent factors in the incoming and outgoing media.
+    T = calculate_transmittace_from_coeff(t, nk_list.at[0].get(), angle_of_incidence, nk_list.at[-1].get(), layer_angles.at[-1].get(), polarization) #calculate T
+    
+    # Return the computed reflectance and transmittance values as the output.
+    return R, T
+
+
+def tmm_incoh_single_wl_angle_point(data: ArrayLike,
+                                    material_distribution: ArrayLike,
+                                    thickness_list: ArrayLike,
+                                    coherent_layer_indices_forward: ArrayLike,
+                                    incoherent_layer_indices_forward: ArrayLike,
+                                    coherency_indices_forward: ArrayLike,
+                                    coherent_layer_indices_backward: ArrayLike,
+                                    coherency_indices_backward: ArrayLike,
+                                    wavelength: ArrayLike,
+                                    angle_of_incidence: ArrayLike,
+                                    polarization: ArrayLike) -> Array:
+    """
+    This function implements the Transfer Matrix Method (TMM) to compute the reflectance (R) 
+    and transmittance (T) of a incoherent multilayer thin-film structure for a given wavelength, 
+    angle of incidence, and polarization.
+
+    Arguments:
+    ----------
+    data: ArrayLike
+        A 2D array where each row corresponds to the refractive index (n) and extinction 
+        coefficient (k) data of a specific material. The first axis length is the number of 
+        materials, and the second axis is the length of the nk vs wavelength data for each 
+        material.
+
+    material_distribution: ArrayLike
+        An array of integers specifying the material index for each layer in the multilayer 
+        structure, including the incoming and outgoing medium. Its length is N + 2, where 
+        N is the number of thin-film layers.
+
+    thickness_list: ArrayLike
+        A 1D array of floats representing the physical thickness of each thin-film layer. 
+        Its length is N, corresponding to the N layers in the multilayer structure.
+
+    coherent_layer_indices_forward: ArrayLike
+        Indices of coherent layers for forward propagation.
+
+    incoherent_layer_indices_forward: ArrayLike
+        Indices of incoherent layers for forward propagation.
+
+    coherency_indices_forward: ArrayLike
+        Indices defining coherency conditions in forward direction.
+
+    coherent_layer_indices_backward: ArrayLike
+        Indices of coherent layers for backward propagation.
+
+    coherency_indices_backward: ArrayLike
+        Indices defining coherency conditions in backward direction.
+
+    wavelength: ArrayLike
+        The wavelength of the light wave (in the same units as the nk data). This is used 
+        to calculate the optical properties for the specific wavelength.
+
+    angle_of_incidence: ArrayLike
+        The angle (in radians) of the incoming light with respect to the normal of the 
+        multilayer thin-film surface.
+
+    polarization: ArrayLike
+        Specifies the polarization state of the light wave. If `False`, the light is 
+        s-polarized (E field is perpendicular to the plane of incidence), and if `True`, 
+        it is p-polarized (E field is parallel to the plane of incidence).
+
+    Returns:
+    --------
+    Tuple of two scalars:
+        R: Reflectance (a value between 0 and 1, representing the ratio of reflected power 
+           to incident power).
+        T: Transmittance (a value between 0 and 1, representing the ratio of transmitted 
+           power to incident power).
+    """
+
+    # Create a list of refractive indices and extinction coefficients for each layer 
+    # at the given wavelength using the provided material distribution.
+    nk_list = create_nk_list(material_distribution, data, wavelength)
+    
+    # Compute the angles of light propagation within each layer based on Snell's Law
+    # and the input angle of incidence.
+    layer_angles = compute_layer_angles(angle_of_incidence, nk_list, polarization)
+    
+    # Compute the z-component of the wavevector (kz) in each layer, which is wavelength-dependent.
+    kz = compute_kz(nk_list, layer_angles, wavelength)
+    
+    # Compute the phase change in each layer due to the thickness of the layer.
+    # Excludes the incoming and outgoing media (index 0 and -1).
+    layer_phases = jnp.multiply(kz.at[1:-1].get(), thickness_list)
+
+    # Compute reflection and transmission coefficients at each interface
+    rt_forward = jnp.squeeze(compute_rt(nk_list = nk_list, angles = layer_angles, polarization = polarization))
+    rt_backward = jnp.squeeze(compute_rt(nk_list = jnp.flip(nk_list), angles = jnp.flip(layer_angles), polarization = polarization))
+
+    # Compute magnitudes of reflection and transmission coefficients for forward propagation (not good for jit! update this func asap)
+    forward_magnitudes = compute_r_t_magnitudes_incoh(coherent_layer_indices = coherent_layer_indices_forward,
+                                                      coherency_indices = coherency_indices_forward,
+                                                      rts = rt_forward,
+                                                      layer_phases = layer_phases,
+                                                      nk_list = nk_list,
+                                                      layer_angles = layer_angles,
+                                                      polarization = polarization)
+    
+    # Compute magnitudes of reflection and transmission coefficients for backward propagation (not good for jit! update this func asap)
+    backward_magnitudes = compute_r_t_magnitudes_incoh(coherent_layer_indices = coherent_layer_indices_backward,
+                                                       coherency_indices = coherency_indices_backward,
+                                                       rts = rt_backward,
+                                                       layer_phases = jnp.flip(layer_phases),
+                                                       nk_list = jnp.flip(nk_list),
+                                                       layer_angles = jnp.flip(layer_angles),
+                                                       polarization = polarization)
+
+    # Flip backward magnitudes to align with the correct propagation direction
+    backward_magnitudes = jnp.flip(backward_magnitudes, axis=0)
+
+    # Compute incoherent layer pass contributions
+    layer_passes = compute_inc_layer_pass(incoherent_layer_indices_forward.at[1:-1].get(), layer_phases)
+
+    # Compute the transfer matrix for the entire multilayer structure
+    tr_matrix = incoh_cascaded_matrix_multiplication(forward_magnitudes = forward_magnitudes.at[1:,:].get(),
+                                                     backward_magnitudes = backward_magnitudes.at[1:,:].get(),
+                                                     layer_passes = layer_passes)
+
+    # Compute the first layer matrix for the incoming medium
+    first_layer_matrix = compute_first_layer_matrix_incoherent(r_forward = forward_magnitudes.at[0,0].get(),
+                                                               t_forward = forward_magnitudes.at[0,1].get(),
+                                                               r_backward = backward_magnitudes.at[0,0].get(),
+                                                               t_backward = backward_magnitudes.at[0,1].get())
+
+    # Multiply the first layer matrix with the transfer matrix
+    tr_matrix = jnp.matmul(first_layer_matrix, tr_matrix)
+
+    # Compute reflectance (R) and transmittance (T) from the transfer matrix elements
+    R = jnp.true_divide(tr_matrix.at[1,0].get(), tr_matrix.at[0,0].get())
+    T = jnp.true_divide(1, tr_matrix.at[0,0].get())
+
+    # Return computed reflectance and transmittance
+    return R, T
+
+@jit
+def tmm_coh_single_wl_angle_point_jit(data: ArrayLike,
+                                      material_distribution: ArrayLike,
+                                      thickness_list: ArrayLike,
+                                      wavelength: ArrayLike,
+                                      angle_of_incidence: ArrayLike,
+                                      polarization: ArrayLike) -> Array:
+    """
+    This function implements the JIT-ed version of Transfer Matrix Method (TMM) to 
+    compute the reflectance (R) and transmittance (T) of a coherent multilayer thin-film 
+    structure for a given wavelength, angle of incidence, and polarization.
+
+    Arguments:
+    ----------
+    data: ArrayLike
+        A 2D array where each row corresponds to the refractive index (n) and extinction 
+        coefficient (k) data of a specific material. The first axis length is the number of 
+        materials, and the second axis is the length of the nk vs wavelength data for each 
+        material.
+
+    material_distribution: ArrayLike
+        An array of integers specifying the material index for each layer in the multilayer 
+        structure, including the incoming and outgoing medium. Its length is N + 2, where 
+        N is the number of thin-film layers.
+
+    thickness_list: ArrayLike
+        A 1D array of floats representing the physical thickness of each thin-film layer. 
+        Its length is N, corresponding to the N layers in the multilayer structure.
+
+    wavelength: ArrayLike
+        The wavelength of the light wave (in the same units as the nk data). This is used 
+        to calculate the optical properties for the specific wavelength.
+
+    angle_of_incidence: ArrayLike
+        The angle (in radians) of the incoming light with respect to the normal of the 
+        multilayer thin-film surface.
+
+    polarization: ArrayLike
+        Specifies the polarization state of the light wave. If `False`, the light is 
+        s-polarized (E field is perpendicular to the plane of incidence), and if `True`, 
+        it is p-polarized (E field is parallel to the plane of incidence).
+
+    Returns:
+    --------
+    Tuple of two scalars:
+        R: Reflectance (a value between 0 and 1, representing the ratio of reflected power 
+           to incident power).
+        T: Transmittance (a value between 0 and 1, representing the ratio of transmitted 
+           power to incident power).
+    """
+
+    # Create a list of refractive indices and extinction coefficients for each layer 
+    # at the given wavelength using the provided material distribution.
+    nk_list = create_nk_list(material_distribution, data, wavelength)
+    
+    # Compute the angles of light propagation within each layer based on Snell's Law
+    # and the input angle of incidence.
+    layer_angles = compute_layer_angles(angle_of_incidence, nk_list, polarization)
+    
+    # Compute the z-component of the wavevector (kz) in each layer, which is wavelength-dependent.
+    kz = compute_kz(nk_list, layer_angles, wavelength)
+    
+    # Compute the phase change in each layer due to the thickness of the layer.
+    # Excludes the incoming and outgoing media (index 0 and -1).
+    layer_phases = jnp.multiply(kz.at[1:-1].get(), thickness_list)
+
+    # Calculate reflection and transmission coefficients at each layer interface.
+    # Excludes the incoming and outgoing media.
+    rt = jnp.squeeze(compute_rt(nk_list = nk_list, angles = layer_angles, polarization = polarization))
+
+    # Multiply transfer matrices for all internal layers to compute the combined matrix.
+    # This represents the cumulative effect of reflections and transmissions.
+    tr_matrix = coh_cascaded_matrix_multiplication(phases = layer_phases, rts = rt.at[1:,:].get())
+
+    # Incorporate the reflection and transmission coefficients of the incoming medium
+    # into the total transfer matrix.
+    tr_matrix = jnp.multiply(jnp.true_divide(1, rt.at[0,1].get()), jnp.dot(jnp.array([[1, rt.at[0,0].get()], [rt.at[0,0].get(), 1]]), tr_matrix))
+
+    # Extract the complex reflectance coefficient (r) from the total transfer matrix.
+    r = jnp.true_divide(tr_matrix.at[1,0].get(), tr_matrix.at[0,0].get())
+
+    # Extract the complex transmittance coefficient (t) from the total transfer matrix.
+    t = jnp.true_divide(1, tr_matrix.at[0,0].get())
+
+    # Compute the reflectance (R) from the reflectance coefficient.
+    R = calculate_reflectance_from_coeff(r)
+
+    # Compute the transmittance (T) from the transmittance coefficient, accounting for
+    # impedance matching and angle-dependent factors in the incoming and outgoing media.
+    T = calculate_transmittace_from_coeff(t, nk_list.at[0].get(), angle_of_incidence, nk_list.at[-1].get(), layer_angles.at[-1].get(), polarization) #calculate T
+    
+    # Return the computed reflectance and transmittance values as the output.
+    return R, T
+
+@jit
+def vectorized_coh_tmm(data: ArrayLike, 
+                       material_distribution: ArrayLike, 
+                       thickness_list: ArrayLike, 
+                       wavelengths: ArrayLike, 
+                       angle_of_incidences: ArrayLike, 
+                       polarization) -> Array:
+    """
+    This function is designed to vectorize the Transfer Matrix Method (TMM) calculations 
+    over two axes: wavelength and angle of incidence. The function uses the `jax.vmap` 
+    function to vectorize the `tmm_coh_single_wl_angle_point` function, which performs TMM 
+    calculations for a single combination of wavelength and angle of incidence. The first 
+    `vmap` maps over the wavelength axis, and the second `vmap` maps over the angle of 
+    incidence axis. The other inputs (`data`, `material_distribution`, `thickness_list`, 
+    and `polarization`) remain constant during the vectorized computation.
+
+    Arguments:
+    ----------
+    data: ArrayLike
+        A 2D array where each row corresponds to the refractive index (n) and extinction 
+        coefficient (k) data of a specific material. The first axis length is the number of 
+        materials, and the second axis is the length of the nk vs wavelength dataset for 
+        each material.
+
+    material_distribution: ArrayLike
+        An array of integers specifying the material index for each layer in the multilayer 
+        structure, including the incoming and outgoing medium. Its length is N + 2, where 
+        N is the number of thin-film layers.
+
+    thickness_list: ArrayLike
+        A 1D array of floats representing the physical thickness of each thin-film layer. 
+        Its length is N, corresponding to the N layers in the multilayer structure.
+
+    wavelengths: ArrayLike
+        The wavelengths of the light wave (in the same units as the nk data). This is used 
+        to calculate the optical properties for the specific wavelength.
+
+    angle_of_incidences: ArrayLike
+        The angles (in radians) of the incoming light with respect to the normal of the 
+        multilayer thin-film surface.
+
+    polarization: ArrayLike
+        Specifies the polarization state of the light wave. If `False`, the light is 
+        s-polarized (perpendicular to the plane of incidence), and if `True`, it is 
+        p-polarized (parallel to the plane of incidence).
+
+    Returns:
+    --------
+    result : Array
+        A multi-dimensional array containing the computed results of the TMM calculations 
+        for all specified wavelengths and angles of incidence. The dimensions of this 
+        array correspond to the vectorized axes (wavelength and angle of incidence).
+    """
+
+    # Use `vmap` to vectorize `tmm_coh_single_wl_angle_point` over wavelength and angle of incidence.
+    # The first `vmap` applies vectorization across the angle_of_incidence axis (last dimension of the input array).
+    # The second `vmap` applies vectorization across the wavelength axis (one level up in the input hierarchy).
+    tmm_vmap = vmap(vmap(tmm_coh_single_wl_angle_point, (None, None, None, 0, None, None)), (None, None, None, None, 0, None))
+    
+    # Apply the vectorized function `tmm_vmap` to the input arguments and return the result.
+    # `data`, `material_distribution`, `thickness_list`, and `polarization` remain constant during vectorized computation
+    return tmm_vmap(data, material_distribution, thickness_list, wavelengths, angle_of_incidences, polarization)
+
+
+def vectorized_incoh_tmm(data: ArrayLike,
+                         material_distribution: ArrayLike,
+                         thickness_list: ArrayLike,
+                         coherent_layer_indices_forward: ArrayLike,
+                         incoherent_layer_indices_forward: ArrayLike,
+                         coherency_indices_forward: ArrayLike,
+                         coherent_layer_indices_backward: ArrayLike,
+                         coherency_indices_backward: ArrayLike,
+                         wavelengths: ArrayLike,
+                         angle_of_incidences: ArrayLike,
+                         polarization: ArrayLike):
+    """
+    This function is designed to vectorize the Transfer Matrix Method (TMM) calculations 
+    over two axes: wavelength and angle of incidence. The function uses the `jax.vmap` 
+    function to vectorize the `tmm_coh_single_wl_angle_point` function, which performs TMM 
+    calculations for a single combination of wavelength and angle of incidence. The first 
+    `vmap` maps over the wavelength axis, and the second `vmap` maps over the angle of 
+    incidence axis. The other inputs (`data`, `material_distribution`, `thickness_list`, 
+    and `polarization`) remain constant during the vectorized computation.
+
+    Arguments:
+    ----------
+    data: ArrayLike
+        A 2D array where each row corresponds to the refractive index (n) and extinction 
+        coefficient (k) data of a specific material. The first axis length is the number of 
+        materials, and the second axis is the length of the nk vs wavelength dataset for 
+        each material.
+
+    material_distribution: ArrayLike
+        An array of integers specifying the material index for each layer in the multilayer 
+        structure, including the incoming and outgoing medium. Its length is N + 2, where 
+        N is the number of thin-film layers.
+
+    thickness_list: ArrayLike
+        A 1D array of floats representing the physical thickness of each thin-film layer. 
+        Its length is N, corresponding to the N layers in the multilayer structure.
+        
+    coherent_layer_indices_forward: ArrayLike
+        A 1D array of indices representing the layers that are considered coherent in the 
+        forward direction.
+
+    incoherent_layer_indices_forward: ArrayLike
+        A 1D array of indices representing the layers that are considered incoherent in the 
+        forward direction. 
+
+    coherency_indices_forward: ArrayLike
+        A 1D array of indices specifying the layers where the coherence condition holds in the 
+        forward direction.
+
+    coherent_layer_indices_backward: ArrayLike
+        A 1D array of indices representing the layers that are considered coherent in the backward
+        direction.
+
+    coherency_indices_backward: ArrayLike
+        A 1D array of indices specifying the layers (coh or incoh) where the coherence condition 
+        holds in the backward direction.
+
+    wavelengths: ArrayLike
+        The wavelengths of the light wave (in the same units as the nk data). This is used 
+        to calculate the optical properties for the specific wavelength.
+
+    angle_of_incidences: ArrayLike
+        The angles (in radians) of the incoming light with respect to the normal of the 
+        multilayer thin-film surface.
+
+    polarization: ArrayLike
+        Specifies the polarization state of the light wave. If `False`, the light is 
+        s-polarized (perpendicular to the plane of incidence), and if `True`, it is 
+        p-polarized (parallel to the plane of incidence).
+
+    Returns:
+    --------
+    result : Array
+        A multi-dimensional array containing the computed results of the TMM calculations 
+        for all specified wavelengths and angles of incidence. The dimensions of this 
+        array correspond to the vectorized axes (wavelength and angle of incidence).
+    """
+
+    # Use `vmap` to vectorize `tmm_incoh_single_wl_angle_point` over wavelength and angle of incidence.
+    # The first `vmap` applies vectorization across the angle_of_incidence axis (last dimension of the input array).
+    # The second `vmap` applies vectorization across the wavelength axis (one level up in the input hierarchy).
+    tmm_vmap = vmap(vmap(tmm_incoh_single_wl_angle_point, 
+                         (None, None, None,None, None, None, None, None, 0, None, None)), 
+                         (None, None, None,None, None, None, None, None, None, 0, None))
+    
+    # Apply the vectorized function `tmm_vmap` to the input arguments and return the result.
+    # `data`, `material_distribution`, `thickness_list`, and `polarization` remain constant during vectorized computation
+    return tmm_vmap(data,
+                    material_distribution,
+                    thickness_list,
+                    coherent_layer_indices_forward,
+                    incoherent_layer_indices_forward,
+                    coherency_indices_forward,
+                    coherent_layer_indices_backward,
+                    coherency_indices_backward,
+                    wavelengths,
+                    angle_of_incidences,
+                    polarization)
+
+def tmm_coh(material_list: List[str],
+            thickness_list: ArrayLike,
+            wavelength_arr: ArrayLike,
+            angle_of_incidences: ArrayLike,
+            polarization: Text) -> Tuple[Array, Array]:
+    """
+    Perform the Transfer Matrix Method (TMM) for coherent multilayer thin films.
+
+    Arguments:
+    ----------
+        material_list (List[str]): A list of material names. Each material is identified by a string.
+            Example: ["Air", "TiO2", "SiO2"]. These strings specify the materials in the multilayer thin film structure.
+            The first and last materials in the list are typically assumed to be semi-infinite layers (e.g., Air, SiO2).
+        
+        thickness_list (jnp.ndarray): An array of thicknesses corresponding to the thin-film layers.
+            Each element specifies the physical thickness of one layer. The order matches the materials in `material_list`.
+            The thicknesses should be finite for thin-film layers and can be ignored for the semi-infinite layers.
+
+        wavelength_arr (jnp.ndarray): An array of wavelengths (in micrometers or nanometers) for which the simulation is performed.
+            This array allows users to specify the range of wavelengths for analyzing the optical properties of the multilayer.
+
+        angle_of_incidences (jnp.ndarray): An array of angles of incidence (in degrees) at which the light interacts with the thin film structure.
+            Each value represents a different incident angle, and the function calculates results for all specified angles.
+
+        polarization (Text): The type of polarization of light, either "s" (s-polarization) or "p" (p-polarization).
+            - "s" refers to perpendicular polarization with respect to the plane of incidence.
+            - "p" refers to parallel polarization with respect to the plane of incidence.
+
+    Returns:
+    --------
+        Tuple[jnp.ndarray, jnp.ndarray]: A tuple of two JAX arrays:
+            - The first array contains the reflection for the given configuration.
+            - The second array contains the transmission for the given configuration.
+            These results describe the amount of light transmitted and reflected at each wavelength and angle of incidence.
+    """
+    
+    # Convert the material list into a set and a material distribution array
+    # The material set contains unique materials, and the distribution describes how materials are layered.
+    material_set, material_distribution = material_distribution_to_set(material_list)
+    
+    # Create the required material data, such as refractive index and extinction coefficient, for each material in the set.
+    # This function retrieves optical properties needed for TMM calculations.
+    data = create_data(material_set)
+
+    # Check the polarization input and convert it to a boolean JAX array.
+    if polarization == 's':
+        # For s-polarization, set the boolean flag to `False`.
+        polarization = jnp.array([False], dtype=bool)
+    elif polarization == 'p':
+        # For p-polarization, set the boolean flag to `True`.
+        polarization = jnp.array([True], dtype=bool)
+    else:
+        # Raise an error if the polarization input is invalid.
+        raise TypeError("The polarization can be 's' or 'p', not other parts. Correct it")
+
+    # Apply the vectorized TMM function, which computes transmission and reflection coefficients
+    # over the specified wavelengths and angles of incidence.
+    # The computation leverages JAX for high-performance array operations.
+    result = vectorized_coh_tmm(data, material_distribution, thickness_list, wavelength_arr, angle_of_incidences, polarization)
+
+    # Return the calculated results, which include transmission and reflection coefficients.
+    return result
+
+def tmm(material_list: List[str],
+        thickness_list: ArrayLike,
+        coherency_list: ArrayLike,
+        wavelength_arr: ArrayLike,
+        angle_of_incidences: ArrayLike,
+        polarization: Text) -> Tuple[Array, Array]:
+    """
+    Perform the Transfer Matrix Method (TMM) for multilayer thin films.
+
+    Arguments:
+    ----------
+        material_list (List[str]): A list of material names. Each material is identified by a string.
+        thickness_list (ArrayLike): An array of thicknesses corresponding to each layer.
+        coherency_list (ArrayLike): An array that specifies the coherency (whether the layer is coherent or incoherent).
+        wavelength_arr (ArrayLike): An array of wavelengths over which to perform the simulation.
+        angle_of_incidences (ArrayLike): An array of angles of incidence.
+        polarization (Text): The type of polarization ('s' for s-polarized or 'p' for p-polarized).
+
+
+    Returns:
+    ----------
+        Tuple[ArrayLike, ArrayLike]: A tuple containing two arrays: 
+            - The first array represents the transmission coefficients.
+            - The second array represents the reflection coefficients.
+    """
+
+    # Convert the material list into a set and a material distribution array
+    # The material set contains unique materials, and the distribution describes how materials are layered.
+    material_set, material_distribution = material_distribution_to_set(material_list)
+
+    # Create the required material data, such as refractive index and extinction coefficient, for each material in the set.
+    # This function retrieves optical properties needed for TMM calculations.
+    data = create_data(material_set)
+
+    # Check the polarization input and convert it to a boolean JAX array.
+    if polarization == 's':
+        # For s-polarization, set the boolean flag to `False`.
+        polarization = jnp.array([False], dtype=bool)
+    elif polarization == 'p':
+        # For p-polarization, set the boolean flag to `True`.
+        polarization = jnp.array([True], dtype=bool)
+    else:
+        # Raise an error if the polarization input is invalid.
+        raise TypeError("The polarization can be 's' or 'p', not other parts. Correct it")
+    
+    # Identify coherent and incoherent layer indices for the forward direction.
+    coherent_groups_forward, incoherent_indices_forward, coherency_indices_forward = find_coh_and_incoh_indices(coherency_list)
+    
+    # Identify coherent and incoherent layer indices for the backward direction (reverse of the coherency list).
+    coherent_groups_backward, _, coherency_indices_backward = find_coh_and_incoh_indices(jnp.flip(coherency_list))
+
+    # Check if the multilayer film is fully coherent (i.e., only 2 incoherent layers).
+    if len(incoherent_indices_forward) == 2:
+        # If the multilayer structure is fully coherent, use the vectorized coherent TMM function.
+        result = vectorized_coh_tmm(data, material_distribution, thickness_list, wavelength_arr, angle_of_incidences, polarization)
+        # Return the result (tuple of transmission and reflection coefficients).
+        return result
+    else:
+        # If the multilayer structure is not fully coherent, use the vectorized incoherent TMM function.
+        result = vectorized_incoh_tmm(data = data,
+                                      material_distribution = material_distribution,
+                                      thickness_list = thickness_list,
+                                      coherent_layer_indices_forward = coherent_groups_forward,
+                                      incoherent_layer_indices_forward = incoherent_indices_forward,
+                                      coherency_indices_forward = coherency_indices_forward,
+                                      coherent_layer_indices_backward = coherent_groups_backward,
+                                      coherency_indices_backward = coherency_indices_backward,
+                                      wavelengths = wavelength_arr,
+                                      angle_of_incidences = angle_of_incidences,
+                                      polarization = polarization)
+        # Return the result (tuple of transmission and reflection coefficients).
+        return result
\ No newline at end of file
diff --git a/tmmax/wavevector.py b/tmmax/wavevector.py
new file mode 100644
index 0000000..33fa4ee
--- /dev/null
+++ b/tmmax/wavevector.py
@@ -0,0 +1,82 @@
+import jax.numpy as jnp # jax's numpy library we will use for all general mathematical operations
+from jax import Array # Type definition for JAX arrays
+from jax.typing import ArrayLike # JAX type hint for array-like objects (supports numpy, JAX arrays, etc.)
+
+def compute_kz(nk_list: ArrayLike,   # The list of complex refractive indices (n + ik) for each medium in the multilayer thin film stack.
+               angles: ArrayLike,    # The angles made by the layer normal and the light ray in each layer (in radians).
+               wavelength: ArrayLike) -> Array:  # The wavelength(s) of the light incident on the multilayer thin film.
+
+    """
+    This function calculates the component of the wavevector (kz) along the z-axis (the direction normal to the surface) 
+    for each layer in a multilayer thin film. It is used in the context of thin film optics, especially when applying 
+    the Transfer Matrix Method (TMM) to model light propagation through multiple layers with different refractive 
+    indices and incident angles. The formula computes kz for each layer based on the refractive index and the angle 
+    of incidence, as well as the wavelength of the light.
+
+    Arguments:
+    - nk_list: A list or array containing the complex refractive index (n + ik) for each layer in the multilayer thin film.
+      For a multilayer thin film with N layers, nk_list consists of N+2 elements. Each element is a complex value representing 
+      the refractive index (n + ik) where 'n' is the real part (refractive index) and 'k' is the imaginary part (extinction coefficient).
+    - angles: An array representing the angle of incidence in each layer, measured between the layer normal and the incoming light ray.
+    - wavelength: A scalar value for the wavelength of the light used in the computation.
+
+    Returns:
+    - An array containing the kz component (wavevector component along the z-axis) for each layer in the thin film. 
+      The kz values are computed using the formula:
+      kz = (2 pi * n * cos(angle)) / wavelength, where 'n' is the complex refractive index of the layer,
+      'angle' is the angle of incidence in each layer, and 'wavelength' is the wavelength of the light.
+    """
+    
+    kz = jnp.true_divide(  # Performs element-wise division of the numerator by the denominator to calculate kz.
+        jnp.multiply(  # Multiplies the complex refractive index, π, and 2.0 to form the numerator.
+            jnp.multiply(jnp.array([2.0]), jnp.pi),  # Multiplies 2.0 with π (constant factor for wavevector calculation).
+            jnp.multiply(nk_list, jnp.cos(angles))  # Multiplies the refractive index of each layer by the cosine of the angle.
+        ), 
+        wavelength  # Divides the result by the wavelength of the light to get the kz component.
+    )
+
+    return kz  # Returns the computed kz values for each layer in the multilayer thin film.
+
+def compute_inc_layer_pass(incoherent_layer_indices: ArrayLike, layer_phases: ArrayLike) -> Array:
+    """
+    This function calculates the fraction of light that successfully passes through the incoherent 
+    layers of an optical multilayer thin film stack. 
+
+    The calculation is based on the exponential decay model for incoherent layers:
+    Pass ratio = exp(-((4π Im[n_i cos(θ_i)]) / λ_vac))
+
+    Also, the phase of a layer is given by:
+
+        2π / λ * n_i * cos(θ_i) * d_i
+
+    This term is already precomputed and given as `layer_phases` in this function. Since we have 
+    already computed these phase values, we do not need to recompute them. Instead, we extract 
+    the imaginary part of these phases, multiply it by -2, and apply the exponential function to 
+    compute the transmission fraction:
+
+    Pass ratio = exp(-2 * Im(layer_phases))
+
+    Parameters:
+    - incoherent_layer_indices (array-like): Indices representing which layers in the multilayer 
+                                             stack are incoherent. This can be a list, numpy array, 
+                                             or jax.numpy array.
+    - layer_phases (array-like): Array of complex phase values for each layer in the stack. These 
+                                 phases encode optical path differences and absorption effects.
+
+    Returns:
+    - Array: An array containing the computed transmission power values for the 
+                         incoherent layers.
+    """
+    # Select values from layer_phases based on the indices of the incoherent layers
+    selected_phases = jnp.take(layer_phases, incoherent_layer_indices)
+    
+    # Extract the imaginary part of the selected phase values (related to absorption)
+    imag_phases = jnp.imag(selected_phases)
+    
+    # Multiply the imaginary part by -2 to match the exponential decay factor
+    transformed_phases = jnp.multiply(-2, imag_phases)
+    
+    # Compute the exponential decay factor representing the fraction of light transmitted
+    result = jnp.exp(transformed_phases)
+
+    return result