{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Silicon–Organic Hybrid Slot Electro-Optic Modulator: RF–Optical Co-Simulation and Vπ·L Extraction\n",
    "\n",
    "In this example, we simulate a silicon–organic hybrid (SOH) electro-optic modulator that overcomes the intrinsic limitations of silicon for efficient electro-optic modulation by using an organic material with a strong Pockels effect inside a slot waveguide. The device concept follows `Heiner Zwickel et al., “Silicon-organic hybrid (SOH) modulators for intensity-modulation/direct-detection links with line rates of up to 120 Gbit/s,” Optics Express 25, 23784 (2017).` DOI: [10.1364/OE.25.023784](https://doi.org/10.1364/OE.25.023784).\n",
    "\n",
    "We build a full cross-section of a ground–signal–ground (GSG) transmission line integrated with a silicon slot waveguide filled with an organic electro-optic material. The RF field from the electrodes modulates the refractive index in the slot through the Pockels effect, which in turn changes the optical effective index.\n",
    "\n",
    "Finally, we compute the electro-optic response and extract the Vπ·L figure of merit from the voltage-dependent change in effective index.\n",
    "\n",
    "### Workflow Overview\n",
    "\n",
    "[1)](#1) Define the full device geometry, including the silicon slot waveguide, organic cladding in the slot, oxide layers, and GSG metal transmission line.\n",
    "\n",
    "[2)](#2) Run an RF mode simulation of the transmission line to obtain the electric field distribution and normalize it to the applied voltage using current and voltage integrals.\n",
    "\n",
    "[3)](#3) Create and run the optical mode simulation.\n",
    "\n",
    "[4)](#4) Use the RF field to compute the Pockels-induced refractive index perturbation in the organic material, solve the optical modes of the perturbed waveguide, and extract Vπ·L from the change in optical effective index versus voltage.\n",
    "\n",
    "\n",
    "<img src=\"img/HybridSiEOM.png\" alt=\"diagram\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.rf as rf\n",
    "from matplotlib import pyplot as plt\n",
    "from tidy3d import web\n",
    "from tidy3d.plugins.mode import ModeSolver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Setup <a name=\"1\"></a>\n",
    "\n",
    "First, we will define an auxiliary function to help create the geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stack_box(\n",
    "    structure_1,\n",
    "    structure_2,\n",
    "    plane=\"x\",\n",
    "    horizontal=\"right\",\n",
    "    vertical=\"top\",\n",
    "    gap_h=0,\n",
    "    gap_v=0,\n",
    "    new_name=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Align and stack ``structure_2`` relative to ``structure_1`` within a given plane.\n",
    "\n",
    "    The function translates ``structure_2`` so that it is positioned next to\n",
    "    ``structure_1`` according to the selected plane and alignment options.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    structure_1 : tidy3d.Structure or geometry with bounding_box\n",
    "        Base object used as the alignment reference.\n",
    "    structure_2 : tidy3d.Structure or geometry with bounding_box\n",
    "        Object to be translated and stacked relative to ``structure_1``.\n",
    "    plane : {'x', 'y', 'z'}, optional\n",
    "        Plane normal along which stacking is defined. Default is ``'x'``.\n",
    "    horizontal : {'left', 'right', 'center'}, optional\n",
    "        Horizontal alignment relative to ``structure_1`` in the chosen plane.\n",
    "        Default is ``'right'``.\n",
    "    vertical : {'top', 'bottom', 'center'}, optional\n",
    "        Vertical alignment relative to ``structure_1`` in the chosen plane.\n",
    "        Default is ``'top'``.\n",
    "    gap_h : float, optional\n",
    "        Gap applied along the horizontal direction. Default is 0.\n",
    "    gap_v : float, optional\n",
    "        Gap applied along the vertical direction. Default is 0.\n",
    "    new_name : str, optional\n",
    "        Name for the updated ``structure_2``. If ``None``, the original name is kept.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tidy3d.Structure or geometry\n",
    "        A translated copy of ``structure_2`` aligned to the new position.\n",
    "    \"\"\"\n",
    "    if isinstance(structure_1, td.Structure):\n",
    "        box_1 = structure_1.geometry.bounding_box\n",
    "    else:\n",
    "        box_1 = structure_1.bounding_box\n",
    "\n",
    "    if isinstance(structure_2, td.Structure):\n",
    "        box_2 = structure_2.geometry.bounding_box\n",
    "    else:\n",
    "        box_2 = structure_2.bounding_box\n",
    "\n",
    "    c1 = np.array(box_1.center)\n",
    "    c2 = np.array(box_2.center)\n",
    "\n",
    "    s1 = np.array(box_1.size)\n",
    "    s2 = np.array(box_2.size)\n",
    "\n",
    "    # Start from the center of structure_1\n",
    "    new_center = c1.copy()\n",
    "\n",
    "    # Axis normal to the stacking plane\n",
    "    axis = {\"x\": 0, \"y\": 1, \"z\": 2}[plane]\n",
    "    aux = [0, 1, 2]\n",
    "    aux.remove(axis)\n",
    "    horizontal_axis = min(aux)\n",
    "    vertical_axis = max(aux)\n",
    "\n",
    "    for align, ax, gap in [\n",
    "        (horizontal, horizontal_axis, gap_h),\n",
    "        (vertical, vertical_axis, gap_v),\n",
    "    ]:\n",
    "        if align == \"center\":\n",
    "            new_center[ax] = c1[ax] + gap\n",
    "        elif align in (\"right\", \"top\"):\n",
    "            new_center[ax] = c1[ax] + s1[ax] / 2 + gap + s2[ax] / 2\n",
    "        elif align in (\"left\", \"bottom\"):\n",
    "            new_center[ax] = c1[ax] - s1[ax] / 2 - gap - s2[ax] / 2\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid alignment option: {align}\")\n",
    "\n",
    "    dx = -c2[0] + new_center[0]\n",
    "    dy = -c2[1] + new_center[1]\n",
    "    dz = -c2[2] + new_center[2]\n",
    "\n",
    "    if isinstance(structure_2, td.Structure):\n",
    "        translated_geometry = structure_2.geometry.translated(x=dx, y=dy, z=dz)\n",
    "        return structure_2.updated_copy(\n",
    "            geometry=translated_geometry,\n",
    "            name=new_name if new_name else structure_2.name,\n",
    "        )\n",
    "\n",
    "    translated_geometry = structure_2.translated(x=dx, y=dy, z=dz)\n",
    "    return translated_geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Media\n",
    "\n",
    "Next, we will define the material properties for both optical and RF frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Material and spectral parameters\n",
    "organic_index = 1.7\n",
    "n_si = 3.45\n",
    "n_sio2 = 1.44\n",
    "\n",
    "# Define materials\n",
    "organic = td.Medium(permittivity=organic_index**2)\n",
    "silicon = td.Medium(permittivity=n_si**2)\n",
    "oxide = td.Medium(permittivity=n_sio2**2)\n",
    "\n",
    "# Metal material from library\n",
    "metal = td.material_library[\"Al\"][\"Rakic1995\"]\n",
    "\n",
    "# Wavelength and frequency definitions\n",
    "lda0 = 1.55\n",
    "ldas = np.linspace(1.50, 1.60, 51)\n",
    "freq0 = td.C_0 / lda0\n",
    "freqs = td.C_0 / ldas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RF material and spectral parameters\n",
    "organic_index_RF = 1.7\n",
    "\n",
    "# Define RF materials\n",
    "organic_RF = td.Medium(permittivity=organic_index_RF**2)\n",
    "silicon_RF = td.Medium(permittivity=11.7)\n",
    "oxide_RF = td.Medium(permittivity=3.9)\n",
    "\n",
    "# RF metal model\n",
    "metal_rf = td.LossyMetalMedium(\n",
    "    frequency_range=(100000000.0, 100000000000.0),\n",
    "    conductivity=41,\n",
    "    fit_param=td.SurfaceImpedanceFitterParam(\n",
    "        max_num_poles=16,\n",
    "    ),\n",
    ")\n",
    "\n",
    "# RF frequency sampling\n",
    "rf_freqs = np.linspace(10e9, 90e9, 21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will define some geometry parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geometric parameters\n",
    "\n",
    "eff_inf = 1e4\n",
    "slot_width = 0.240\n",
    "slot_height = 0.220\n",
    "slot_separation = 0.120\n",
    "rail_thickness = 0.070\n",
    "rail_width = 1\n",
    "substrate_thickness = 1\n",
    "oxide_thickness = 1\n",
    "clad_thickness = 0.5\n",
    "cpw_gap = 1\n",
    "cpw_thickness = 0.5\n",
    "cpw_width = 2\n",
    "organic_slot_width = cpw_gap * 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the necessary geometries and stack them to build the full device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geometry primitives\n",
    "\n",
    "si_substrate = td.Box.from_bounds(\n",
    "    rmin=(-eff_inf, -eff_inf, -eff_inf),\n",
    "    rmax=(eff_inf, eff_inf, substrate_thickness),\n",
    ")\n",
    "\n",
    "sio2_substrate = td.Box(center=(0, 0, 0), size=(eff_inf, eff_inf, oxide_thickness))\n",
    "\n",
    "sio2_clad = td.Box(center=(0, 0, 0), size=(eff_inf, eff_inf, clad_thickness))\n",
    "\n",
    "signal = td.Box(center=(0, 0, 0), size=(eff_inf, cpw_width, cpw_thickness))\n",
    "\n",
    "ground = td.Box(center=(0, 0, 0), size=(eff_inf, eff_inf, cpw_thickness))\n",
    "\n",
    "si_rail = td.Box(center=(0, 0, 0), size=(eff_inf, rail_width, rail_thickness))\n",
    "\n",
    "slot_waveguide = td.Box(center=(0, 0, 0), size=(eff_inf, slot_width, slot_height))\n",
    "\n",
    "organic_slot = td.Box(center=(0, 0, 0), size=(eff_inf, organic_slot_width, clad_thickness))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we combine all geometries and define the full structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stack geometries to build the full structure\n",
    "\n",
    "sio2_substrate = stack_box(\n",
    "    si_substrate, sio2_substrate, plane=\"x\", vertical=\"top\", horizontal=\"center\"\n",
    ")\n",
    "\n",
    "sio2_clad = stack_box(sio2_substrate, sio2_clad, plane=\"x\", vertical=\"top\", horizontal=\"center\")\n",
    "\n",
    "signal = stack_box(\n",
    "    sio2_clad,\n",
    "    signal,\n",
    "    plane=\"x\",\n",
    "    vertical=\"top\",\n",
    "    horizontal=\"center\",\n",
    "    new_name=\"signal\",\n",
    ")\n",
    "\n",
    "ground_left = stack_box(\n",
    "    signal,\n",
    "    ground,\n",
    "    plane=\"x\",\n",
    "    vertical=\"center\",\n",
    "    horizontal=\"left\",\n",
    "    gap_h=cpw_gap,\n",
    "    new_name=\"ground_left\",\n",
    ")\n",
    "\n",
    "ground_right = stack_box(\n",
    "    signal,\n",
    "    ground,\n",
    "    plane=\"x\",\n",
    "    vertical=\"center\",\n",
    "    horizontal=\"right\",\n",
    "    gap_h=cpw_gap,\n",
    "    new_name=\"ground_right\",\n",
    ")\n",
    "\n",
    "center_gap = cpw_gap / 2 + cpw_width / 2\n",
    "\n",
    "slot_waveguide_left = stack_box(\n",
    "    sio2_substrate,\n",
    "    slot_waveguide,\n",
    "    plane=\"x\",\n",
    "    vertical=\"top\",\n",
    "    horizontal=\"center\",\n",
    "    gap_h=-slot_separation / 2 - slot_width / 2 - center_gap,\n",
    "    new_name=\"slot_waveguide_left\",\n",
    ")\n",
    "\n",
    "slot_waveguide_right = stack_box(\n",
    "    sio2_substrate,\n",
    "    slot_waveguide,\n",
    "    plane=\"x\",\n",
    "    vertical=\"top\",\n",
    "    horizontal=\"center\",\n",
    "    gap_h=slot_separation / 2 + slot_width / 2 - center_gap,\n",
    "    new_name=\"slot_waveguide_right\",\n",
    ")\n",
    "\n",
    "organic_slot_left = stack_box(\n",
    "    sio2_substrate,\n",
    "    organic_slot,\n",
    "    plane=\"x\",\n",
    "    vertical=\"top\",\n",
    "    horizontal=\"center\",\n",
    "    gap_h=-center_gap,\n",
    "    new_name=\"organic_slot_left\",\n",
    ")\n",
    "\n",
    "si_rail_left = stack_box(\n",
    "    sio2_substrate,\n",
    "    si_rail,\n",
    "    plane=\"x\",\n",
    "    vertical=\"top\",\n",
    "    horizontal=\"center\",\n",
    "    gap_h=-slot_separation / 2 - slot_width / 2 - rail_width / 2 - center_gap,\n",
    "    new_name=\"si_rail\",\n",
    ")\n",
    "\n",
    "si_rail_right = stack_box(\n",
    "    sio2_substrate,\n",
    "    si_rail,\n",
    "    plane=\"x\",\n",
    "    vertical=\"top\",\n",
    "    horizontal=\"center\",\n",
    "    gap_h=slot_separation / 2 + slot_width / 2 + rail_width / 2 - center_gap,\n",
    "    new_name=\"si_rail_right\",\n",
    ")\n",
    "\n",
    "# Mirror the organic slot to the opposite side\n",
    "organic_slot_right = organic_slot_left.translated(x=0, y=2 * center_gap, z=0)\n",
    "\n",
    "# Combine waveguide geometries\n",
    "waveguide_left_geom = slot_waveguide_left + slot_waveguide_right + si_rail_left + si_rail_right\n",
    "\n",
    "substrate = td.Structure(geometry=si_substrate, medium=silicon)\n",
    "\n",
    "waveguide_right_geom = waveguide_left_geom.translated(x=0, y=2 * center_gap, z=0)\n",
    "\n",
    "sio2 = td.Structure(geometry=sio2_substrate + sio2_clad, medium=oxide)\n",
    "\n",
    "waveguide = td.Structure(geometry=waveguide_left_geom + waveguide_right_geom, medium=silicon)\n",
    "\n",
    "\n",
    "cpw = td.Structure(\n",
    "    geometry=signal + ground_left + ground_right,\n",
    "    medium=metal,\n",
    ")\n",
    "\n",
    "organic_fill = td.Structure(geometry=organic_slot_left + organic_slot_right, medium=organic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create two separate structure lists: one for the optical simulation and one for the RF simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Structure collections for different simulation domains\n",
    "\n",
    "structures_RF = [\n",
    "    substrate.updated_copy(medium=silicon_RF),\n",
    "    sio2.updated_copy(medium=oxide_RF),\n",
    "    cpw.updated_copy(medium=metal_rf),\n",
    "    organic_fill.updated_copy(medium=organic_RF),\n",
    "    waveguide.updated_copy(medium=silicon_RF),\n",
    "]\n",
    "\n",
    "structures_optical = [sio2, cpw, organic_fill, waveguide]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we place everything in a [Scene](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Scene.html) object and visualize the final setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and visualize the RF scene cross-section\n",
    "\n",
    "scene = td.Scene(structures=structures_RF)\n",
    "ax = scene.plot(x=0)\n",
    "ax.set_xlim(-4, 4)\n",
    "ax.set_ylim(0, 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RF Simulation <a name=\"2\"></a>\n",
    "\n",
    "Now we will define a [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) object to calculate the RF modes.\n",
    "\n",
    "First, we define a [LayerRefinementSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LayerRefinementSpec.html) to refine the mesh around the metal structures and set the global mesh configuration, adding a [MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.2/api/_autosummary/tidy3d.MeshOverrideStructure.html) to properly resolve the optical waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Layer refinement around CPW structures\n",
    "refinement = 10\n",
    "lr_spec = td.LayerRefinementSpec.from_structures(\n",
    "    structures=[cpw],\n",
    "    min_steps_along_axis=5,\n",
    "    corner_refinement=td.GridRefinement(dl=cpw_thickness / refinement, num_cells=5),\n",
    "    refinement_inside_sim_only=False,\n",
    ")\n",
    "\n",
    "# Mesh override for the waveguide region (finer mesh in the z-direction)\n",
    "mins, maxs = slot_waveguide_left.bounds\n",
    "center_waveguide = 0.5 * (np.array(mins) + np.array(maxs))\n",
    "\n",
    "override_region = td.MeshOverrideStructure(\n",
    "    geometry=td.Box(\n",
    "        size=(eff_inf, slot_width, slot_height),\n",
    "        center=(0, -center_gap, center_waveguide[2]),\n",
    "    ),\n",
    "    dl=(None, 0.02, 0.02),\n",
    ")\n",
    "\n",
    "# Global grid specification for the RF simulation\n",
    "grid_spec_RF = td.GridSpec.auto(\n",
    "    min_steps_per_sim_size=150,\n",
    "    wavelength=td.C_0 / max(rf_freqs),\n",
    "    layer_refinement_specs=[lr_spec],\n",
    "    override_structures=[override_region],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will define an auxiliary function to correctly place the integral paths for calculating impedance and voltage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_gsg_integrals(ground_left, signal, ground_right, plane=\"x\"):\n",
    "    \"\"\"\n",
    "    Create current and voltage integrals for a GSG (ground-signal-ground) CPW cross-section.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ground_left : tidy3d.Structure or geometry\n",
    "        Left ground conductor.\n",
    "    signal : tidy3d.Structure or geometry\n",
    "        Signal conductor.\n",
    "    ground_right : tidy3d.Structure or geometry\n",
    "        Right ground conductor.\n",
    "    plane : {'x', 'y', 'z'}, optional\n",
    "        Normal axis of the cross-sectional plane. Default is ``'x'``.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tuple\n",
    "        (I_int, V_int) current and voltage integral objects.\n",
    "    \"\"\"\n",
    "    # Extract geometries if structures are provided\n",
    "    if isinstance(ground_left, td.Structure):\n",
    "        ground_left_geom = ground_left.geometry\n",
    "    else:\n",
    "        ground_left_geom = ground_left\n",
    "\n",
    "    if isinstance(signal, td.Structure):\n",
    "        signal_geom = signal.geometry\n",
    "    else:\n",
    "        signal_geom = signal\n",
    "\n",
    "    # Determine in-plane axes\n",
    "    axis = {\"x\": 0, \"y\": 1, \"z\": 2}[plane]\n",
    "    aux = [0, 1, 2]\n",
    "    aux.remove(axis)\n",
    "    horizontal_axis = min(aux)\n",
    "    vertical_axis = max(aux)\n",
    "\n",
    "    gap = signal_geom.bounds[0][horizontal_axis] - ground_left_geom.bounds[1][horizontal_axis]\n",
    "\n",
    "    height = signal_geom.bounding_box.size[vertical_axis] * 2\n",
    "    width = signal_geom.bounding_box.size[horizontal_axis] + gap\n",
    "\n",
    "    # Current integration loop around the signal conductor\n",
    "    I_int = rf.AxisAlignedCurrentIntegral(\n",
    "        center=signal_geom.bounding_box.center,\n",
    "        size=(\n",
    "            0,\n",
    "            width,\n",
    "            height,\n",
    "        ),\n",
    "        sign=\"+\",\n",
    "    )\n",
    "\n",
    "    center_gap = list(signal_geom.bounding_box.center)\n",
    "    center_gap[horizontal_axis] = signal_geom.bounds[1][horizontal_axis] + gap / 2\n",
    "    size = [0, 0, 0]\n",
    "    size[horizontal_axis] = gap\n",
    "\n",
    "    # Voltage integration path from signal to ground\n",
    "    V_int = rf.AxisAlignedVoltageIntegral(\n",
    "        center=center_gap,\n",
    "        size=size,\n",
    "        sign=\"-\",\n",
    "    )\n",
    "\n",
    "    return I_int, V_int\n",
    "\n",
    "\n",
    "I_int, V_int = add_gsg_integrals(ground_left, signal, ground_right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now define our [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) object and visualize the setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:53:41 -03 </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: RF simulations and functionality will require new license </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">requirements in an upcoming release. All RF-specific classes are   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">now available within the sub-package </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d.rf'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains a </span><span style=\"color: #008000; text-decoration-color: #008000\">'LossyMetalMedium'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                  </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:53:41 -03\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: RF simulations and functionality will require new license \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mrequirements in an upcoming release. All RF-specific classes are   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mnow available within the sub-package \u001b[0m\u001b[32m'tidy3d.rf'\u001b[0m\u001b[31m.                  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m - Contains a \u001b[0m\u001b[32m'LossyMetalMedium'\u001b[0m\u001b[31m.                                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RF simulation setup\n",
    "sim_RF = td.Simulation(\n",
    "    grid_spec=grid_spec_RF,\n",
    "    structures=structures_RF,\n",
    "    center=signal.bounding_box.center,\n",
    "    size=(10, 100, 100),\n",
    "    run_time=1e-12,\n",
    ")\n",
    "\n",
    "# RF mode solver configuration\n",
    "mode_RF = ModeSolver(\n",
    "    simulation=sim_RF,\n",
    "    plane=td.Box(\n",
    "        center=signal.bounding_box.center,\n",
    "        size=(0, 100, 100),\n",
    "    ),\n",
    "    mode_spec=td.ModeSpec(num_modes=10, target_neff=1.5),\n",
    "    freqs=[rf_freqs[5]],\n",
    ")\n",
    "\n",
    "# Plot modes and grid\n",
    "ax = mode_RF.plot()\n",
    "mode_RF.plot_grid(ax=ax)\n",
    "ax.set_xlim(-3, 0)\n",
    "ax.set_ylim(0, 5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the integral paths to ensure they are defined correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot voltage and current integration paths\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "mode_RF.plot(ax=ax)\n",
    "I_int.plot(x=0, ax=ax)\n",
    "V_int.plot(x=0, ax=ax)\n",
    "ax.set_xlim(-5, 5)\n",
    "ax.set_ylim(0, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can run the RF mode solver and process the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:53:43 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'mode_solver_RF'</span> with resource_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c'</span> and task_type            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'MODE_SOLVER'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:53:43 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_solver_RF'\u001b[0m with resource_id                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c'\u001b[0m and task_type            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'MODE_SOLVER'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4f3f-a086-fa1be7189e7c'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=613237;https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=188024;https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=613237;https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=76822;https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=613237;https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\u001b\\\u001b[32m-3c7ac757-adb0-\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=613237;https://tidy3d.simulation.cloud/workbench?taskId=mo-3c7ac757-adb0-4f3f-a086-fa1be7189e7c\u001b\\\u001b[32m4f3f-a086-fa1be7189e7c'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-f9e994c1-6315-4b44-af3a-c2cb1435b23a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=72311;https://tidy3d.simulation.cloud/folders/folder-f9e994c1-6315-4b44-af3a-c2cb1435b23a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3eb7f230bf564775a4dcc1615dc93513",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:53:46 -03 </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:53:46 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.007\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:53:49 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:53:49 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8298aba56e804e6ab0f6dab1c7e01126",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:00 -03 </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:00 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the RF mode solver simulation\n",
    "data_RF = web.run(mode_RF, task_name=\"mode_solver_RF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can visualize the RF fields and inspect their overlap with the optical waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: RF simulations and functionality will require new license </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">requirements in an upcoming release. All RF-specific classes are   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">now available within the sub-package </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d.rf'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains a </span><span style=\"color: #008000; text-decoration-color: #008000\">'LossyMetalMedium'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains monitors defined for RF wavelengths.                   </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: RF simulations and functionality will require new license \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mrequirements in an upcoming release. All RF-specific classes are   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mnow available within the sub-package \u001b[0m\u001b[32m'tidy3d.rf'\u001b[0m\u001b[31m.                  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m - Contains a \u001b[0m\u001b[32m'LossyMetalMedium'\u001b[0m\u001b[31m.                                  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31m - Contains monitors defined for RF wavelengths.                   \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the magnitude of the Ey field for the first mode\n",
    "ax = mode_RF.plot_field(\"Ey\", \"abs\", mode_index=0, robust=False, vmax=15, shading=\"gouraud\")\n",
    "ax.set_xlim(0.5, 2.5)\n",
    "ax.set_ylim(1.5, 4.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optical Simulation <a name=\"3\"></a>\n",
    "\n",
    "Next, we define an optical simulation and solve for the propagating mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation domain, sources, and monitors\n",
    "\n",
    "sim_size = (10, 10, 5)\n",
    "sim_center = (0, 0, 4)\n",
    "\n",
    "half_z = 0.5 * sim_size[2]\n",
    "substrate_thickness = 2.0\n",
    "substrate_center_z = -half_z + substrate_thickness / 2\n",
    "\n",
    "substrate_geom = td.Box(\n",
    "    center=(0, 0, substrate_center_z),\n",
    "    size=(sim_size[0], sim_size[1], substrate_thickness),\n",
    ")\n",
    "\n",
    "substrate = td.Structure(geometry=substrate_geom, medium=oxide, name=\"substrate\")\n",
    "\n",
    "# Optical grid specification\n",
    "grid_spec = td.GridSpec.auto(\n",
    "    min_steps_per_wvl=50,\n",
    "    override_structures=[],\n",
    "    wavelength=1.55,\n",
    ")\n",
    "\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=organic_index)\n",
    "source_span_y = max(slot_width, 4 * rail_width)\n",
    "source_span_z = 3 * slot_height\n",
    "\n",
    "# Field monitor in a cross-sectional plane\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(0, sim_size[1], sim_size[2]),\n",
    "    freqs=[freq0],\n",
    "    name=\"field_slice\",\n",
    ")\n",
    "\n",
    "simulation_optical = td.Simulation(\n",
    "    size=sim_size,\n",
    "    center=sim_center,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=structures_optical,\n",
    "    sources=[],\n",
    "    monitors=[],\n",
    "    run_time=1e-12,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Optical mode solver setup\n",
    "mode_solver_center = (0, center_gap, center_waveguide[2])\n",
    "mode_solver_size = (0, 3, 3)\n",
    "\n",
    "ms = ModeSolver(\n",
    "    simulation=simulation_optical,\n",
    "    plane=td.Box(\n",
    "        center=mode_solver_center,\n",
    "        size=mode_solver_size,\n",
    "    ),\n",
    "    mode_spec=td.ModeSpec(num_modes=2),\n",
    "    freqs=[freq0],\n",
    ")\n",
    "\n",
    "# Plot the computed modes\n",
    "ms.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:01 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'mode_solver'</span> with resource_id                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8'</span> and task_type            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'MODE_SOLVER'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:01 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_solver'\u001b[0m with resource_id                        \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8'\u001b[0m and task_type            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'MODE_SOLVER'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">490b-aec1-d385d5b8a9f8'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=168430;https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=216388;https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=168430;https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=966854;https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=168430;https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\u001b\\\u001b[32m-34b3abf4-9dd0-\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=168430;https://tidy3d.simulation.cloud/workbench?taskId=mo-34b3abf4-9dd0-490b-aec1-d385d5b8a9f8\u001b\\\u001b[32m490b-aec1-d385d5b8a9f8'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-f9e994c1-6315-4b44-af3a-c2cb1435b23a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=842890;https://tidy3d.simulation.cloud/folders/folder-f9e994c1-6315-4b44-af3a-c2cb1435b23a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a350004a47b74313a2b3dbeef5ecc2eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:04 -03 </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.013</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:04 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.013\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:06 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:06 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b178872fe0b42ba8831cd18f128b979",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:16 -03 </span>Loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:16 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the optical mode solver simulation\n",
    "mode_data = web.run(ms, task_name=\"mode_solver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the magnitude of the Ey field for the first optical mode\n",
    "ax = ms.plot_field(\"Ey\", \"abs\", mode_index=0, robust=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pockels Effect Calculation <a name=\"4\"></a>\n",
    "\n",
    "Now that we have the optical simulation and the RF fields, we can perturb the optical simulation by considering the Pockels effect.\n",
    "\n",
    "We first normalize the RF fields by the voltage, use the formula for the Pockels effect\n",
    "\n",
    "$$-0.5 n_0^3 r_{33} E$$\n",
    "\n",
    "and create a perturbed medium for the polymer extraordinary axis using a [CustomAnisotropicMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomAnisotropicMedium.html) object.\n",
    "\n",
    "Finally, we scale it for different voltages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perturbed_solver(Voltage, absolute=True):\n",
    "    \"\"\"\n",
    "    Build a perturbed optical simulation and mode solver under an applied voltage.\n",
    "\n",
    "    The refractive index of the organic region is modified using the\n",
    "    electro-optic effect based on the RF electric field distribution.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    Voltage : float\n",
    "        Applied voltage used to scale the refractive index perturbation.\n",
    "    absolute : bool, optional\n",
    "        If True, use the magnitude of the RF electric field. If False,\n",
    "        use the real part. Default is True.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tuple\n",
    "        (perturbed_sim, perturbed_mode_solver)\n",
    "    \"\"\"\n",
    "    e_field = data_RF.Ey\n",
    "    V0 = abs(V_int.compute_voltage(data_RF).sel(mode_index=0).isel(f=0, drop=True))\n",
    "    r33 = 144e-6  # µm/V\n",
    "    n0 = 1.7\n",
    "\n",
    "    if absolute:\n",
    "        e_norm = (e_field / V0.real).isel(mode_index=0, f=0, drop=True).abs\n",
    "    else:\n",
    "        e_norm = (e_field / V0.real).isel(mode_index=0, f=0, drop=True).real\n",
    "\n",
    "    delta_n = -0.5 * n0**3 * r33 * e_norm\n",
    "\n",
    "    eps_o_array = td.SpatialDataArray(\n",
    "        np.full((1, 1, 1), n0**2), coords={\"x\": [0], \"y\": [0], \"z\": [0]}\n",
    "    )\n",
    "    eps_e_array = (n0 + delta_n * Voltage) ** 2\n",
    "\n",
    "    perturbed_medium = td.CustomAnisotropicMedium(\n",
    "        xx=td.CustomMedium(permittivity=eps_o_array, subpixel=True),\n",
    "        yy=td.CustomMedium(permittivity=eps_e_array, subpixel=True),\n",
    "        zz=td.CustomMedium(permittivity=eps_o_array, subpixel=True),\n",
    "    )\n",
    "\n",
    "    perturbed_organic_fill = organic_fill.updated_copy(medium=perturbed_medium)\n",
    "    perturbed_sim = simulation_optical.updated_copy(\n",
    "        structures=[sio2, cpw, perturbed_organic_fill, waveguide]\n",
    "    )\n",
    "    perturbed_mode_solver = ModeSolver(\n",
    "        simulation=perturbed_sim,\n",
    "        plane=ms.plane,\n",
    "        mode_spec=td.ModeSpec(num_modes=1),\n",
    "        freqs=[freq0],\n",
    "    )\n",
    "    return perturbed_sim, perturbed_mode_solver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating $V_\\pi$\n",
    "\n",
    "To calculate $V_\\pi$, we create a batch of simulations for different voltages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1218a6c199e44abd870c458eecbdd29f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:28 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:28 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m11\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:54:47 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.115</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:54:47 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.115\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d3ff48cc00b4fedae53aa991f24c852",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:55:16 -03 </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m18:55:16 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run perturbed mode solvers for a range of applied voltages\n",
    "voltages = np.arange(11)\n",
    "sims = {str(v): perturbed_solver(v)[1] for v in voltages}\n",
    "\n",
    "perturbed_data = web.run(\n",
    "    sims,\n",
    "    task_name=\"perturbed_mode_solver\",\n",
    "    reduce_simulation=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract effective index for the fundamental mode at each voltage\n",
    "n_eff = [sim_data.n_eff.isel(mode_index=0, f=0).item() for sim_data in perturbed_data.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot effective index versus applied voltage\n",
    "_ = plt.plot(voltages, n_eff, \".-\")\n",
    "_ = plt.ticklabel_format(style=\"plain\", useOffset=False)\n",
    "plt.gca().set(xlabel=\"Voltage (V)\", ylabel=\"Optical Effective Index\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vπ·L (push-pull) = 0.48 V·cm\n"
     ]
    }
   ],
   "source": [
    "# The variation is approximately linear, so use the full voltage range\n",
    "dneff_dV = abs(n_eff[-1] - n_eff[0]) / (voltages[-1] - voltages[0])\n",
    "\n",
    "vpil = 0.5 * 1.55 / dneff_dV  # V·µm\n",
    "\n",
    "print(f\"Vπ·L (push-pull) = {vpil * 1e-4 / 2:.2f} V·cm\")"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Active photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to simulate a silicon–organic hybrid electro-optic modulator with RF–optical co-simulation and Vπ·L extraction using Tidy3D.",
  "feature_image": "./img/HybridSiEOM.png",
  "features": [
   "Perturbation medium"
  ],
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "keywords": "silicon–organic hybrid, electro-optic modulator, Pockels effect, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "title": "How to model a silicon–organic hybrid modulator using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}