{ "cells": [ { "cell_type": "markdown", "id": "8d226695-57c5-49b3-baf5-18b10feae3d8", "metadata": {}, "source": [ "# Carrier injection based Mach-Zehnder modulator\n", "\n", "Tidy3D capabilities include the modeling of material properties perturbations due to the presence of free carriers. This provides a convenient way to perform full-wave simulations of electro-optic modulators. In this notebook we demonstrate simulation of a simple pin-junction electro-optic modulator as part of an MZI. For generating free carrier distributions under different voltages we use our [Charge solver](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.HeatChargeSimulation.html#tidy3d.HeatChargeSimulation). This example loosely follows the experimental modulator described in [Zhou Liang et al 2011 Chinese Phys. Lett. 28 074202](https://doi.org/10.1088/0256-307X/28/7/074202).\n", "\n", "\"Schematic" ] }, { "cell_type": "markdown", "id": "aac1b9c6-9ffb-4c76-a2d9-7142ccba382a", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Problem Parameters" ] }, { "cell_type": "code", "execution_count": 1, "id": "db1f6c6d-85f9-4fba-a3bb-42a50a455ed3", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:38.229687Z", "iopub.status.busy": "2025-05-15T10:48:38.229590Z", "iopub.status.idle": "2025-05-15T10:48:39.511686Z", "shell.execute_reply": "2025-05-15T10:48:39.511406Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import photonforge as pf\n", "import tidy3d as td\n", "from matplotlib import pyplot as plt\n", "from tidy3d import web" ] }, { "cell_type": "markdown", "id": "067d95d0-6f97-4373-b9d2-cb44052b3d89", "metadata": {}, "source": [ "Our simulation setup will consist of an MZI with two arms, one of which has a common pin-junction profile. For combiner and splitter of the signal we use compact y-junction MMIs. The overall geometry and notation used is shown in the following figure.\n", "\n", "\"Modulator\n", "\n", "\"Scheme" ] }, { "cell_type": "code", "execution_count": 2, "id": "f29bad36-ed7d-47e2-a5b4-64912f68e4c2", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.513015Z", "iopub.status.busy": "2025-05-15T10:48:39.512789Z", "iopub.status.idle": "2025-05-15T10:48:39.515023Z", "shell.execute_reply": "2025-05-15T10:48:39.514775Z" } }, "outputs": [], "source": [ "# modulator cross section parameters (um)\n", "w_core = 0.5\n", "h_core = 0.34\n", "h_slab = 0.1\n", "h_side = 0.34\n", "w_contact = 0.5\n", "x_side = 2.25\n", "x_total = 3\n", "x_i = 0.55\n", "x_p = 1.75\n", "h_contact = 0.1\n", "# note that the height of the metal contact doesn't affect the results\n", "# but devsim requires creating a region representing it\n", "# in order to create a interface between the metal contact and modulator\n", "\n", "# modulator doping concentrations (1/cm^3)\n", "conc_p = 1e19\n", "conc_pp = 5e19\n", "conc_n = 1e19\n", "conc_nn = 5e19\n", "# note that concentrations in ++/-- are (conc_p + conc_pp) and (conc_n + conc_nn)\n", "\n", "# photonic circuit geometric parameters (um)\n", "y_length_in = 10\n", "y_length_out = 1\n", "y_length_bend = 10\n", "wg_spacing = 3.5\n", "taper_length = 10\n", "pin_length = 200\n", "\n", "# effective infinity\n", "effective_inf = 1e6" ] }, { "cell_type": "markdown", "id": "006de98d-fdb1-4d18-b58c-4aa99ca23ff4", "metadata": {}, "source": [ "Auxiliary variables for easier construction of simulations." ] }, { "cell_type": "code", "execution_count": 3, "id": "7b8204ef-3864-4b0e-b14e-ddb72525a84a", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.515776Z", "iopub.status.busy": "2025-05-15T10:48:39.515704Z", "iopub.status.idle": "2025-05-15T10:48:39.517173Z", "shell.execute_reply": "2025-05-15T10:48:39.516985Z" } }, "outputs": [], "source": [ "mzi_length = 2 * taper_length + pin_length\n", "splitter_length = y_length_in + y_length_out + y_length_bend\n", "\n", "z_core = h_core / 2\n", "z_slab = h_slab / 2" ] }, { "cell_type": "markdown", "id": "aaf448bf-8c7d-485f-a958-603d9f6a685e", "metadata": {}, "source": [ "## Semiconductor medium\n", "\n", "Before we can start the Charge simulation, let us create a semiconductor medium. Semiconductor mediums are constructed with the class [SemiconductorMedium](url=https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.SemiconductorMedium.html#tidy3d.SemiconductorMedium). \n", "\n", "Since semiconductor mediums accept doping, let's create those first. Doping is defined here in terms of boxes. Among the different types of doping boxes available we'll be using here the [ConstantDoping](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.ConstantDoping.html#tidy3d.ConstantDoping), which applies a constant doping to the doping box. One thing to note is that these doping boxes are additive, i.e., if two donor doping boxes overlap, the total concentration in the overlap region will be the sum of these two overlapping doping boxes. To further demonstrate this concept:" ] }, { "cell_type": "code", "execution_count": 4, "id": "3096b943-7a7d-4e2c-afdb-3c11ab336b70", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.517962Z", "iopub.status.busy": "2025-05-15T10:48:39.517809Z", "iopub.status.idle": "2025-05-15T10:48:39.520196Z", "shell.execute_reply": "2025-05-15T10:48:39.520026Z" } }, "outputs": [], "source": [ "acceptors = []\n", "donors = []\n", "\n", "acceptors.append(\n", " td.ConstantDoping.from_bounds(\n", " rmin=[-effective_inf, wg_spacing / 2 - x_total, -1],\n", " rmax=[effective_inf, wg_spacing / 2 - x_p, h_side + 1],\n", " concentration=conc_pp,\n", " )\n", ")\n", "acceptors.append(\n", " td.ConstantDoping.from_bounds(\n", " rmin=[-effective_inf, wg_spacing / 2 - x_total, -1],\n", " rmax=[effective_inf, wg_spacing / 2 - x_i, h_side + 1],\n", " concentration=conc_p,\n", " )\n", ")\n", "\n", "donors.append(\n", " td.ConstantDoping.from_bounds(\n", " rmin=[-effective_inf, wg_spacing / 2 + x_p, -1],\n", " rmax=[effective_inf, wg_spacing / 2 + x_total, h_side + 1],\n", " concentration=conc_nn,\n", " )\n", ")\n", "donors.append(\n", " td.ConstantDoping.from_bounds(\n", " rmin=[-effective_inf, wg_spacing / 2 + x_i, -1],\n", " rmax=[effective_inf, wg_spacing / 2 + x_total, h_side + 1],\n", " concentration=conc_n,\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "f39fd3a4-eb98-4a81-a957-8a20396e99a5", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.520998Z", "iopub.status.busy": "2025-05-15T10:48:39.520857Z", "iopub.status.idle": "2025-05-15T10:48:39.523298Z", "shell.execute_reply": "2025-05-15T10:48:39.523060Z" } }, "outputs": [ { "data": { "text/html": [ "
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       "             in future versions. Please use 'ConstantEffectiveDOS' instead.     \n",
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       "             in future versions. Please use 'ConstantEffectiveDOS' instead.     \n",
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       "             in future versions. Please use 'ConstantEnergyBandGap' instead.    \n",
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Please use \u001b[0m\u001b[32m'ConstantEnergyBandGap'\u001b[0m\u001b[31m instead. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# let's define a material here for our Charge simulations\n", "si_doped = td.MultiPhysicsMedium(\n", " optical=td.material_library[\"cSi\"][\"Li1993_293K\"],\n", " charge=td.SemiconductorMedium(\n", " permittivity=11.1,\n", " N_c=td.ConstantEffectiveDOS(N=2.86e19),\n", " N_v=td.ConstantEffectiveDOS(N=3.1e19),\n", " E_g=td.ConstantEnergyBandGap(eg=1.11),\n", " mobility_n=td.ConstantMobilityModel(mu=400),\n", " mobility_p=td.ConstantMobilityModel(mu=200),\n", " R=[td.ShockleyReedHallRecombination(tau_n=1e-8, tau_p=1e-8)],\n", " N_a=acceptors,\n", " N_d=donors,\n", " ),\n", ")" ] }, { "cell_type": "markdown", "id": "28c3236d-8367-490b-962d-b19cf96c20ad", "metadata": {}, "source": [ "### Optic mediums\n", "Since some of the structures will be reused in the optic simulations, let us also create the required mediums here.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "b502d09f-c425-4ece-818b-09d18a81e80b", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.524145Z", "iopub.status.busy": "2025-05-15T10:48:39.524031Z", "iopub.status.idle": "2025-05-15T10:48:39.525878Z", "shell.execute_reply": "2025-05-15T10:48:39.525651Z" } }, "outputs": [], "source": [ "wvl_um = 1.55\n", "freq0 = td.C_0 / wvl_um\n", "\n", "si = si_doped.optical\n", "n_si, k_si = si.nk_model(frequency=td.C_0 / wvl_um)\n", "\n", "si_non_perturb = td.Medium.from_nk(n=n_si, k=k_si, freq=freq0)\n", "\n", "sio2 = td.Medium(permittivity=1.444**2)" ] }, { "cell_type": "markdown", "id": "60f46abf-01b7-4aa5-bc89-590f6875cd3f", "metadata": {}, "source": [ "## Charge simulation\n", "\n", "We are now ready to create the charge simulation. The first step is to create the waveguide structures. To do this, we define the following functions that encapsulate the necessary steps.\n", "\n", "We will use PhotonForge’s layout capabilities. PhotonForge also provides a complete set of tools for photonic design automation, including PDK integration, parametric components, connectivity management, and technology definitions. You can check the many applications of this powerful tool [here](https://docs.flexcompute.com/projects/photonforge/en/latest/examples.html).\n", "\n", "The `make_waveguide` function is a generic function that creates a tapered waveguide with initial width `wg_width_0` and final width `wg_width_1`. This is easily achieved using [pf.Path](https://docs.flexcompute.com/projects/photonforge/en/latest/_autosummary/photonforge.Path.html) and `pf.Path.segment`." ] }, { "cell_type": "code", "execution_count": 7, "id": "a8c29a1b-85f5-40b6-b03e-4fe955ae7202", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.526735Z", "iopub.status.busy": "2025-05-15T10:48:39.526584Z", "iopub.status.idle": "2025-05-15T10:48:39.528691Z", "shell.execute_reply": "2025-05-15T10:48:39.528487Z" } }, "outputs": [], "source": [ "def make_waveguide(\n", " x0, y0, z0, x1, y1, wg_width_0, wg_width_1, wg_thickness, medium, sidewall_angle=0\n", "):\n", " \"\"\"\n", " Linear waveguide taper using PhotonForge.\n", " \"\"\"\n", "\n", " # create path starting at (x0, y0)\n", " path = pf.Path(origin=(x0, y0), width=wg_width_0)\n", "\n", " # linear taper to (x1, y1)\n", " path.segment((x1, y1), width=wg_width_1)\n", "\n", " # convert to polygon\n", " poly = path.to_polygon()\n", "\n", " # create PolySlab geometry\n", " geometry = td.PolySlab(\n", " vertices=poly.vertices,\n", " axis=2,\n", " slab_bounds=(z0, z0 + wg_thickness),\n", " sidewall_angle=sidewall_angle,\n", " )\n", "\n", " return td.Structure(geometry=geometry, medium=medium)" ] }, { "cell_type": "code", "execution_count": 8, "id": "4697870a-e8f0-4bea-9074-d7bb6502e49c", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.529552Z", "iopub.status.busy": "2025-05-15T10:48:39.529444Z", "iopub.status.idle": "2025-05-15T10:48:39.532176Z", "shell.execute_reply": "2025-05-15T10:48:39.531900Z" } }, "outputs": [], "source": [ "def make_rib_waveguide(\n", " x0,\n", " y0,\n", " z0,\n", " x1,\n", " y1,\n", " core_width,\n", " slab_width,\n", " side_width,\n", " core_thickness,\n", " slab_thickness,\n", " side_thickness,\n", " medium,\n", " sidewall_angle=0,\n", "):\n", " \"\"\"\n", " This function defines a linear waveguide taper and returns the tidy3d structure of it.\n", "\n", " Parameters\n", " ----------\n", " x0: x coordinate of the waveguide starting position (um)\n", " y0: y coordinate of the waveguide starting position (um)\n", " z0: z coordinate of the waveguide bottom surface (um)\n", " x1: x coordinate of the waveguide end position (um)\n", " y1: y coordinate of the waveguide end position (um)\n", " core_width: width of the waveguide core (um)\n", " slab_width: width of the slab (um)\n", " side_width: width of the side ribs (um)\n", " core_thickness: thickness of the waveguide core (um)\n", " slab_thickness: thickness of the slab (um)\n", " side_thickness: thickness of the side ribs (um)\n", " medium: medium of the waveguide\n", " sidewall_angle: side wall angle of the waveguide (rad)\n", " \"\"\"\n", "\n", " # modulator\n", " slab = make_waveguide(\n", " x0=x0,\n", " y0=y0,\n", " z0=z0,\n", " x1=x1,\n", " y1=y1,\n", " wg_width_0=slab_width,\n", " wg_width_1=slab_width,\n", " wg_thickness=slab_thickness,\n", " medium=medium,\n", " sidewall_angle=sidewall_angle,\n", " )\n", "\n", " core = make_waveguide(\n", " x0=x0,\n", " y0=y0,\n", " z0=z0,\n", " x1=x1,\n", " y1=y1,\n", " wg_width_0=core_width,\n", " wg_width_1=core_width,\n", " wg_thickness=core_thickness,\n", " medium=medium,\n", " sidewall_angle=sidewall_angle,\n", " )\n", "\n", " y_side_top = y0 + (slab_width / 2 - side_width / 2)\n", " y_side_bottom = y0 - (slab_width / 2 - side_width / 2)\n", "\n", " side_top = make_waveguide(\n", " x0=x0,\n", " y0=y_side_top,\n", " z0=z0,\n", " x1=x1,\n", " y1=y_side_top,\n", " wg_width_0=side_width,\n", " wg_width_1=side_width,\n", " wg_thickness=side_thickness,\n", " medium=medium,\n", " sidewall_angle=sidewall_angle,\n", " )\n", "\n", " side_bottom = make_waveguide(\n", " x0=x0,\n", " y0=y_side_bottom,\n", " z0=z0,\n", " x1=x1,\n", " y1=y_side_bottom,\n", " wg_width_0=side_width,\n", " wg_width_1=side_width,\n", " wg_thickness=side_thickness,\n", " medium=medium,\n", " sidewall_angle=sidewall_angle,\n", " )\n", "\n", " return [core, slab, side_top, side_bottom]" ] }, { "cell_type": "markdown", "id": "6981efea-c7c4-48c0-bd29-a24ddbe8d3d8", "metadata": {}, "source": [ "\n", "With the above functions we can now create the waveguide structures `pin_wg`. For ease in defining boundary conditions we define here some auxiliary structures made up of a conductive medium defined with [ChargeConductorMedium](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.ChargeConductorMedium.html#tidy3d.ChargeConductorMedium)." ] }, { "cell_type": "code", "execution_count": 9, "id": "c892232b-f288-49db-a9a9-e7f9bdeecd3e", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.533424Z", "iopub.status.busy": "2025-05-15T10:48:39.533324Z", "iopub.status.idle": "2025-05-15T10:48:39.536973Z", "shell.execute_reply": "2025-05-15T10:48:39.536765Z" } }, "outputs": [], "source": [ "# top arm: PIN region\n", "pin_wg = make_rib_waveguide(\n", " x0=-pin_length / 2,\n", " y0=wg_spacing / 2,\n", " z0=0,\n", " x1=pin_length / 2,\n", " y1=wg_spacing / 2,\n", " core_width=w_core,\n", " slab_width=2 * x_total,\n", " side_width=x_total - x_side,\n", " core_thickness=h_core,\n", " slab_thickness=h_slab,\n", " side_thickness=h_side,\n", " medium=si_doped,\n", " sidewall_angle=0,\n", ")\n", "\n", "# auxiliary materials we use to define BCs\n", "aux_medium = td.MultiPhysicsMedium(\n", " charge=td.ChargeConductorMedium(conductivity=1), name=\"aux_medium\"\n", ")\n", "\n", "# create a couple structs to define the contacts\n", "contact_p = td.Structure(\n", " geometry=td.Box(\n", " center=(0, wg_spacing / 2 - x_total + w_contact / 2, h_side + h_contact / 2),\n", " size=(effective_inf, w_contact, h_contact),\n", " ),\n", " medium=aux_medium,\n", " name=\"contact_p\",\n", ")\n", "\n", "contact_n = td.Structure(\n", " geometry=td.Box(\n", " center=(0, wg_spacing / 2 + x_total - w_contact / 2, h_side + h_contact / 2),\n", " size=(effective_inf, w_contact, h_contact),\n", " ),\n", " medium=aux_medium,\n", " name=\"contact_n\",\n", ")" ] }, { "cell_type": "markdown", "id": "3d0fd17d-6777-4c09-a34a-3b41be803508", "metadata": {}, "source": [ "### Charge scene\n", "Now let's put these structures together and check that it all looks good." ] }, { "cell_type": "code", "execution_count": 10, "id": "45be7323-cb5f-49a9-85b1-4196e59c5852", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.537794Z", "iopub.status.busy": "2025-05-15T10:48:39.537702Z", "iopub.status.idle": "2025-05-15T10:48:39.591465Z", "shell.execute_reply": "2025-05-15T10:48:39.590954Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data", "transient": {} } ], "source": [ "scene_charge = td.Scene(\n", " structures=pin_wg + [contact_p, contact_n],\n", " medium=sio2,\n", ")\n", "\n", "scene_charge.plot(x=0)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "88b5f8c9-3ccf-4f09-903f-f991ca0dfbd5", "metadata": {}, "source": [ "Let's also inspect the doping profiles and make sure it is what we were expecting." ] }, { "cell_type": "code", "execution_count": 11, "id": "3a34a78b-bc2a-4ebb-9e66-902b9810e97c", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.592601Z", "iopub.status.busy": "2025-05-15T10:48:39.592365Z", "iopub.status.idle": "2025-05-15T10:48:39.819234Z", "shell.execute_reply": "2025-05-15T10:48:39.818931Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data", "transient": {} } ], "source": [ "scene_charge.plot_structures_property(x=0, property=\"doping\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "cd1219bd-e5cc-4c1a-a542-5a195d2a2589", "metadata": {}, "source": [ "### Boundary conditions\n", "Since we're interested in the response of the system for different applied voltages, we'll need to solve the charge problem at each of these voltages. \n", "In Charge this can readily be done since the [VoltageBC](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.VoltageBC.html#tidy3d.VoltageBC) can accept an array of voltages as source through [DCVoltageSource](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.DCVoltageSource.html#tidy3d.DCVoltageSource). A parameter scan will be run and the returned data will have the provided voltage values as a separate dimension.\n", " \n", "\n", "Let's define forward bias values up to 1.2 V with a step of 0.1 V." ] }, { "cell_type": "code", "execution_count": 12, "id": "bb1b8222-baf4-45e7-b4d5-f51a3bfe39a2", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.820371Z", "iopub.status.busy": "2025-05-15T10:48:39.820271Z", "iopub.status.idle": "2025-05-15T10:48:39.822583Z", "shell.execute_reply": "2025-05-15T10:48:39.822352Z" } }, "outputs": [], "source": [ "# create BCs\n", "voltages = list(np.arange(13) * 0.1)\n", "\n", "bc_v1 = td.HeatChargeBoundarySpec(\n", " condition=td.VoltageBC(source=td.DCVoltageSource(voltage=voltages)),\n", " placement=td.StructureBoundary(structure=contact_p.name),\n", ")\n", "\n", "bc_v2 = td.HeatChargeBoundarySpec(\n", " condition=td.VoltageBC(source=td.DCVoltageSource(voltage=0)),\n", " placement=td.StructureBoundary(structure=contact_n.name),\n", ")\n", "\n", "boundary_conditions = [bc_v1, bc_v2]" ] }, { "cell_type": "markdown", "id": "2f38e0bd-e357-4c95-bb32-79eee66252b4", "metadata": {}, "source": [ "### Charge monitors\n", "Since we're interested in obtaining the free carrier distribution we'll add a [SteadyFreeCarrierMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.SteadyFreeCarrierMonitor.html#tidy3d.SteadyFreeCarrierMonitor) to our Charge simulation. Note that the below monitor has been defined in the $x=0$ plane." ] }, { "cell_type": "code", "execution_count": 13, "id": "0e17f8ec-90c5-47d9-a27d-625485da65e7", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.823613Z", "iopub.status.busy": "2025-05-15T10:48:39.823541Z", "iopub.status.idle": "2025-05-15T10:48:39.825191Z", "shell.execute_reply": "2025-05-15T10:48:39.824989Z" } }, "outputs": [], "source": [ "charge_mnt = td.SteadyFreeCarrierMonitor(\n", " center=(0, 0, 0),\n", " size=(0, effective_inf, effective_inf),\n", " name=\"charge_mnt\",\n", " unstructured=True,\n", ")" ] }, { "cell_type": "markdown", "id": "27261923-f2e6-4d2b-b6d6-468236aa9ef1", "metadata": {}, "source": [ "### Charge simulation object\n", "When running Charge, we need to define the type of Charge simulation and some convergence settings. In the current case we set an relative tolerance of $10^{-5}$ and an absolute tolerance of $5\\cdot 10^{10}$. The absolute tolerance may seem big though one should notice we have variables (electrons/holes) that take on values many orders of magnitude larger than the tolerance ($\\approx 10^{20}$).\n", "\n", "In the current case, we are going to run an isothermal DC case which we can define with `IsothermalSteadyChargeDCAnalysis`. In DC mode we can set the parameter `convergence_dv` which tells the solver to limit the size of the sweep, i.e., if we need to solve for a bias of 0 and 0.5 and we set `convergence_dv=0.1`, it will force the solver to go between 0 and 0.5 at intervals of 0.1. \n", "\n", "We'll use a spatial resolution of 0.005 $\\mu m$." ] }, { "cell_type": "code", "execution_count": 14, "id": "3ad1489b-d1c4-4adb-acd3-deae329052ff", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.826068Z", "iopub.status.busy": "2025-05-15T10:48:39.825923Z", "iopub.status.idle": "2025-05-15T10:48:39.827684Z", "shell.execute_reply": "2025-05-15T10:48:39.827450Z" } }, "outputs": [], "source": [ "convergence_settings = td.ChargeToleranceSpec(rel_tol=1e-5, abs_tol=5e10, max_iters=400)\n", "\n", "analysis_type = td.IsothermalSteadyChargeDCAnalysis(\n", " temperature=300, tolerance_settings=convergence_settings, convergence_dv=0.1\n", ")\n", "\n", "res = 0.005\n", "mesh = td.UniformUnstructuredGrid(dl=res, relative_min_dl=0)" ] }, { "cell_type": "markdown", "id": "81568295-1d27-4f0e-8113-7a14732d08e7", "metadata": {}, "source": [ "\n", "We now have all the required elements to define a Charge simulation object. Note that the simulation has 0 size in the $x$ direction. With this, we'll make sure that the simulation is 2D even if the structures themselves are not." ] }, { "cell_type": "code", "execution_count": 15, "id": "5ea0497d-adb6-4e04-83df-75a66420a43e", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.828553Z", "iopub.status.busy": "2025-05-15T10:48:39.828454Z", "iopub.status.idle": "2025-05-15T10:48:39.930250Z", "shell.execute_reply": "2025-05-15T10:48:39.930023Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
12:41:19 UTC WARNING: Structure at 'structures[0]' has bounds that extend       \n",
       "             exactly to simulation edges. This can cause unexpected behavior. If\n",
       "             intending to extend the structure to infinity along one dimension, \n",
       "             use td.inf as a size variable instead to make this explicit.       \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m12:41:19 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data", "transient": {} }, { "data": { "text/html": [ "
             WARNING: Suppressed 11 WARNING messages.                           \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m11\u001b[0m\u001b[31m WARNING messages. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "charge_sim = td.HeatChargeSimulation(\n", " sources=[],\n", " monitors=[charge_mnt],\n", " analysis_spec=analysis_type,\n", " center=(0, wg_spacing / 2, (h_side + h_contact) / 2),\n", " size=(0, 2 * x_total, h_side + h_contact),\n", " structures=scene_charge.structures,\n", " medium=scene_charge.medium,\n", " boundary_spec=boundary_conditions,\n", " grid_spec=mesh,\n", " symmetry=(0, 0, 0),\n", ")" ] }, { "cell_type": "markdown", "id": "49cc9230-affb-4ed8-b53b-a8d32ad351d9", "metadata": {}, "source": [ "\n", "We can also plot here the simulation and some properties. In the properties plot, as well as the conductivity (which is invisible since the only conducting structures are the BC auxiliary ones) we can see the boundary conditions in blue and orange." ] }, { "cell_type": "code", "execution_count": 16, "id": "46dcd634-d5f7-4def-801a-5e9172ef1677", "metadata": { "execution": { "iopub.execute_input": "2025-05-15T10:48:39.931325Z", "iopub.status.busy": "2025-05-15T10:48:39.931236Z", "iopub.status.idle": "2025-05-15T10:48:40.034183Z", "shell.execute_reply": "2025-05-15T10:48:40.033951Z" } }, "outputs": [ { "data": { "image/png": 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", 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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
12:41:35 UTC Loading results from charge_mzi_pin.hdf5                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:41:35 UTC\u001b[0m\u001b[2;36m \u001b[0mLoading results from charge_mzi_pin.hdf5                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Structure at 'structures[0]' has bounds that extend       \n",
       "             exactly to simulation edges. This can cause unexpected behavior. If\n",
       "             intending to extend the structure to infinity along one dimension, \n",
       "             use td.inf as a size variable instead to make this explicit.       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Suppressed 11 WARNING messages.                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m11\u001b[0m\u001b[31m WARNING messages.                           \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Warning messages were found in the solver log. For more   \n",
       "             information, check 'SimulationData.log' or use                     \n",
       "             'web.download_log(task_id)'.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "charge_data = web.run(charge_sim, task_name=\"mzi_pin\", path=\"charge_mzi_pin.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5b8bf3-1f6b-40d5-9808-10476d42d519",
   "metadata": {},
   "source": [
    "### Carrier Distribution\n",
    "\n",
    "Let's visualize the electron distributions for zero and 1.2 V biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee0af0d0-ef1a-4889-af3d-fbd7c133c6cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:11.324760Z",
     "iopub.status.busy": "2025-05-15T10:57:11.324680Z",
     "iopub.status.idle": "2025-05-15T10:57:13.066379Z",
     "shell.execute_reply": "2025-05-15T10:57:13.065823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 1, figsize=(10, 3))\n",
    "\n",
    "for ind, volt in enumerate([voltages[0], voltages[-1]]):\n",
    "    charge_data[\"charge_mnt\"].electrons.sel(x=0, voltage=volt).plot(ax=ax[ind], grid=False)\n",
    "    ax[ind].set_title(f\"Bias: {volt:1.1f} V\")\n",
    "    ax[ind].set_xlabel(\"y (um)\")\n",
    "    ax[ind].set_ylabel(\"z (um)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8c028d-7042-4db1-8d28-cd27567f0c6f",
   "metadata": {},
   "source": [
    "## Optic Simulations\n",
    "\n",
    "Having obtained free carrier solutions in the modulator cross section we can now turn to setting up optic simulation that will use these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179baf1e-d107-470c-819f-4dc07ca381d3",
   "metadata": {},
   "source": [
    "We will use empiric relationships presented in [M. Nedeljkovic, R. Soref and G. Z. Mashanovich, \"Free-Carrier Electrorefraction and Electroabsorption Modulation Predictions for Silicon Over the 1–14-  μm  Infrared Wavelength Range,\" IEEE Photonics Journal, vol. 3, no. 6, pp. 1171-1180, Dec. 2011](https://doi.org/10.1109/JPHOT.2011.2171930), that state that changes in $n$ and $k$ of Si can be described by formulas\n",
    "$$ - \\Delta n = \\frac{dn}{dN_e}(\\lambda) (\\Delta N_e)^{\\alpha(\\lambda)} + \\frac{dn}{dN_h}(\\lambda) (\\Delta N_h)^{\\beta(\\lambda)}$$\n",
    "$$ \\Delta \\left( \\frac{4 \\pi k}{\\lambda} \\right) = \\frac{dk}{dN_e}(\\lambda) (\\Delta N_e)^{\\gamma(\\lambda)} + \\frac{dk}{dN_h}(\\lambda) (\\Delta N_h)^{\\delta(\\lambda)}$$\n",
    "where $\\Delta N_e$ and $\\Delta N_h$ are electron and hole densities, and parameters have the following values for wavelength of 1.55 $\\mu$m:\n",
    "\n",
    "| $\\lambda$ | $\\frac{dn}{dN_e}$ | $\\alpha$ | $\\frac{dn}{dN_h}$ | $\\beta$ | $\\frac{dk}{dN_e}$ | $\\gamma$ | $\\frac{dk}{dN_h}$ | $\\delta$ |\n",
    "| --------- | ----------------- | ------- | ------------------ | ------- | ----------------- | -------- | ----------------- | -------- |\n",
    "| $1.55$ | $5.40 \\times 10^{-22}$ | $1.011$ | $1.53 \\times 10^{-18}$ | $0.838$ | $8.88 \\times 10^{-21}$ | $1.167$ | $5.84 \\times 10^{-20}$ | $1.109$ | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be12ebdf-6a0f-40a0-8f17-2f6d68362eb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.067813Z",
     "iopub.status.busy": "2025-05-15T10:57:13.067661Z",
     "iopub.status.idle": "2025-05-15T10:57:13.070442Z",
     "shell.execute_reply": "2025-05-15T10:57:13.069958Z"
    }
   },
   "outputs": [],
   "source": [
    "ne_coeff = -5.4e-22\n",
    "ne_pow = 1.011\n",
    "\n",
    "nh_coeff = -1.53e-18\n",
    "nh_pow = 0.838\n",
    "\n",
    "k_factor = wvl_um * 1e-4 / 4 / np.pi  # factor for conversion from absorption coefficient into k\n",
    "\n",
    "ke_coeff = k_factor * 8.88e-21\n",
    "ke_pow = 1.167\n",
    "\n",
    "kh_coeff = k_factor * 5.84e-20\n",
    "kh_pow = 1.109"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd7b930-fc76-48fe-bb64-7a990a3e0824",
   "metadata": {},
   "source": [
    "Given the nonlinear character of these dependencies we will incorporate them as sampled function on a rectangular grid formed by electron and hole density values. Specifically, we will sample the given $n$ and $k$ dependencies in the electron and hole density ranges up to $10^{20}$ 1/$\\text{cm}^3$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dcd5d66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max electron density exponent: 20\n",
      "Max hole density exponent: 20\n"
     ]
    }
   ],
   "source": [
    "max_electron_exp = np.ceil(\n",
    "    np.log10(charge_data[\"charge_mnt\"].electrons.sel(x=0, y=2, z=0, method=\"nearest\").values.max())\n",
    ")\n",
    "max_holes_exp = np.ceil(\n",
    "    np.log10(charge_data[\"charge_mnt\"].holes.sel(x=0, y=2, z=0, method=\"nearest\").values.max())\n",
    ")\n",
    "\n",
    "print(f\"Max electron density exponent: {max_electron_exp:.0f}\")\n",
    "print(f\"Max hole density exponent: {max_holes_exp:.0f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b5b40237-9f65-45d9-8ef9-89b402965317",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.071642Z",
     "iopub.status.busy": "2025-05-15T10:57:13.071501Z",
     "iopub.status.idle": "2025-05-15T10:57:13.074245Z",
     "shell.execute_reply": "2025-05-15T10:57:13.073842Z"
    }
   },
   "outputs": [],
   "source": [
    "Ne_range = np.concatenate(([0], np.logspace(15, max_electron_exp, 20)))\n",
    "Nh_range = np.concatenate(([0], np.logspace(15, max_holes_exp, 21)))\n",
    "\n",
    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing=\"ij\")\n",
    "\n",
    "dn_mesh = ne_coeff * Ne_mesh**ne_pow + nh_coeff * Nh_mesh**nh_pow\n",
    "dk_mesh = ke_coeff * Ne_mesh**ke_pow + kh_coeff * Nh_mesh**kh_pow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae434a1-c5e8-4484-afaf-0e90928b8a7c",
   "metadata": {},
   "source": [
    "Now we convert sampled values of $n$ and $k$ into permittivity $\\varepsilon$ and conductivity $\\sigma$ values, and assemble a non-dispersive medium with perturbations (`PerturbationMedium`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d55cd927-8bd8-4136-b057-e1b9dace539a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:57:13.081787Z"
    }
   },
   "outputs": [],
   "source": [
    "dn_data = td.ChargeDataArray(dn_mesh, coords=dict(n=Ne_range, p=Nh_range))\n",
    "dk_data = td.ChargeDataArray(dk_mesh, coords=dict(n=Ne_range, p=Nh_range))\n",
    "\n",
    "n_si_charge = td.CustomChargePerturbation(perturbation_values=dn_data)\n",
    "k_si_charge = td.CustomChargePerturbation(perturbation_values=dk_data)\n",
    "\n",
    "n_si_perturbation = td.ParameterPerturbation(\n",
    "    charge=n_si_charge,\n",
    ")\n",
    "\n",
    "k_si_perturbation = td.ParameterPerturbation(\n",
    "    charge=k_si_charge,\n",
    ")\n",
    "\n",
    "si_perturb = td.PerturbationMedium.from_unperturbed(\n",
    "    medium=si_non_perturb,\n",
    "    perturbation_spec=td.IndexPerturbation(\n",
    "        delta_n=n_si_perturbation,\n",
    "        delta_k=k_si_perturbation,\n",
    "        freq=freq0,\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70cf327-d013-48f6-84c8-532fb6ed3ff1",
   "metadata": {},
   "source": [
    "### Circuits Structures\n",
    "For generating the entire circuit we use helper functions for creating a single waveguide and a waveguide y-junction (see tutorials [Defining common integrated photonic components](https://www.flexcompute.com/tidy3d/examples/notebooks/PICComponents/) and [Waveguide Y junction](https://www.flexcompute.com/tidy3d/examples/notebooks/YJunction/))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0616fae7-dff3-4505-a901-ffde55a507ab",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:57:13.088802Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_y_junction(\n",
    "    x0,\n",
    "    y0,\n",
    "    z0,\n",
    "    wg_thickness,\n",
    "    wg_spacing,\n",
    "    wg_length_in,\n",
    "    wg_length_out,\n",
    "    bend_length,\n",
    "    direction,\n",
    "    medium,\n",
    "    sidewall_angle=0,\n",
    "):\n",
    "\n",
    "    w1, w2, w3, w4, w5 = 0.5, 0.5, 0.6, 0.7, 0.9\n",
    "    w6, w7, w8, w9, w10 = 1.26, 1.4, 1.4, 1.4, 1.4\n",
    "    w11, w12, w13 = 1.31, 1.2, 1.2\n",
    "\n",
    "    l_junction = 2\n",
    "    if wg_length_in < l_junction:\n",
    "        raise ValueError(f\"'wg_length_in' cannot be less than {l_junction}.\")\n",
    "\n",
    "    slab_bounds = (z0, z0 + wg_thickness)\n",
    "    h_bend = (wg_spacing - w13 + w1) / 2\n",
    "\n",
    "    x1 = x0 - direction * wg_length_out\n",
    "    x2 = x1 - direction * bend_length\n",
    "    x3 = x2 - direction * l_junction\n",
    "    x4 = x2 - direction * wg_length_in\n",
    "\n",
    "    y_out = y0 + wg_spacing / 2\n",
    "    yb = y0 + w13 / 2 - w1 / 2\n",
    "\n",
    "    bend_dir = (direction, 0)\n",
    "\n",
    "    # input straight\n",
    "    wg_in = pf.Path(origin=(x4, y0), width=w1).segment(endpoint=(x3, y0))\n",
    "\n",
    "    # junction taper\n",
    "    x_j = np.linspace(x3, x2, 13)\n",
    "    w_j = np.array([w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12, w13])\n",
    "    x_poly = np.concatenate((x_j, x_j[::-1]))\n",
    "    y_poly = y0 + np.concatenate((w_j / 2, -w_j[::-1] / 2))\n",
    "    junction = pf.Polygon(list(zip(x_poly, y_poly)))\n",
    "\n",
    "    # bends\n",
    "    bend_up = pf.Path(origin=(x2, yb), width=w1).s_bend(\n",
    "        (direction * bend_length, h_bend), direction=bend_dir, relative=True\n",
    "    )\n",
    "    bend_down = pf.Path(origin=(x2, y0 - (yb - y0)), width=w1).s_bend(\n",
    "        (direction * bend_length, -h_bend), direction=bend_dir, relative=True\n",
    "    )\n",
    "\n",
    "    # output straights\n",
    "    straight_up = pf.Path(origin=(x1, y_out), width=w1).segment(endpoint=(x0, y_out))\n",
    "    straight_down = pf.Path(origin=(x1, y0 - (y_out - y0)), width=w1).segment(\n",
    "        endpoint=(x0, y0 - (y_out - y0))\n",
    "    )\n",
    "\n",
    "    polygons = pf.boolean(\n",
    "        [wg_in, junction, bend_up, bend_down, straight_up, straight_down],\n",
    "        [],\n",
    "        \"+\",\n",
    "    )\n",
    "    assert len(polygons) == 1, f\"Expected 1 polygon, got {len(polygons)}\"\n",
    "    polygon = polygons[0]\n",
    "    assert len(polygon.holes) == 0\n",
    "\n",
    "    geometry = td.PolySlab(\n",
    "        vertices=polygon.vertices,\n",
    "        axis=2,\n",
    "        slab_bounds=slab_bounds,\n",
    "        sidewall_angle=sidewall_angle,\n",
    "    )\n",
    "    return td.Structure(geometry=geometry, medium=medium)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9dc90cb-7ef9-4676-b5d4-930dfd51e224",
   "metadata": {},
   "source": [
    "Additionally, we create convenience functions to create a rib waveguide and a strip-to-rib taper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e1c06d14-d50f-4c96-b88c-639e9a053e7a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:57:13.092623Z"
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   },
   "outputs": [],
   "source": [
    "def make_strip_rib_taper(\n",
    "    x0,\n",
    "    y0,\n",
    "    z0,\n",
    "    x1,\n",
    "    y1,\n",
    "    core_width,\n",
    "    slab_width,\n",
    "    core_thickness,\n",
    "    slab_thickness,\n",
    "    medium,\n",
    "    sidewall_angle=0,\n",
    "):\n",
    "    \"\"\"\n",
    "    This function defines a linear waveguide taper and returns the tidy3d structure of it.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x0: x coordinate of the waveguide starting position (um)\n",
    "    y0: y coordinate of the waveguide starting position (um)\n",
    "    z0: z coordinate of the waveguide bottom surface (um)\n",
    "    x1: x coordinate of the waveguide end position (um)\n",
    "    y1: y coordinate of the waveguide end position (um)\n",
    "    core_width: width of the waveguide core (um)\n",
    "    slab_width: width of the slab (um)\n",
    "    core_thickness: thickness of the waveguide core (um)\n",
    "    slab_thickness: thickness of the slab (um)\n",
    "    medium: medium of the waveguide\n",
    "    sidewall_angle: side wall angle of the waveguide (rad)\n",
    "    \"\"\"\n",
    "\n",
    "    # core\n",
    "    core = make_waveguide(\n",
    "        x0=x0,\n",
    "        y0=y0,\n",
    "        z0=z0,\n",
    "        x1=x1,\n",
    "        y1=y1,\n",
    "        wg_width_0=core_width,\n",
    "        wg_width_1=core_width,\n",
    "        wg_thickness=core_thickness,\n",
    "        medium=medium,\n",
    "        sidewall_angle=sidewall_angle,\n",
    "    )\n",
    "\n",
    "    # slab\n",
    "    slab = make_waveguide(\n",
    "        x0=x0,\n",
    "        y0=y0,\n",
    "        z0=z0,\n",
    "        x1=x1,\n",
    "        y1=y1,\n",
    "        wg_width_0=core_width,\n",
    "        wg_width_1=slab_width,\n",
    "        wg_thickness=slab_thickness,\n",
    "        medium=medium,\n",
    "        sidewall_angle=sidewall_angle,\n",
    "    )\n",
    "\n",
    "    return [core, slab]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f34e057-8da4-4592-9b6a-d0ab814d1760",
   "metadata": {},
   "source": [
    "Now we need to create necessary components. Note that we use the medium with perturbation models only for the modulator section (`pin_wg`) as the rest of the structures are assumed to be undoped. Note that we're recreating the waveguide in the PIN region so that we can use the perturbed silicon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cdbc8475-00d5-45cd-80b2-5760090448a8",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2025-05-15T10:57:13.102301Z",
     "shell.execute_reply": "2025-05-15T10:57:13.101965Z"
    }
   },
   "outputs": [],
   "source": [
    "# input coupler\n",
    "coupler_in = make_y_junction(\n",
    "    x0=-mzi_length / 2,\n",
    "    y0=0,\n",
    "    z0=0,\n",
    "    wg_thickness=h_core,\n",
    "    wg_spacing=wg_spacing,\n",
    "    wg_length_in=y_length_in,\n",
    "    wg_length_out=y_length_out,\n",
    "    bend_length=y_length_bend,\n",
    "    direction=1,\n",
    "    medium=si_non_perturb,\n",
    "    sidewall_angle=0,\n",
    ")\n",
    "\n",
    "# output coupler\n",
    "coupler_out = make_y_junction(\n",
    "    x0=mzi_length / 2,\n",
    "    y0=0,\n",
    "    z0=0,\n",
    "    wg_thickness=h_core,\n",
    "    wg_spacing=wg_spacing,\n",
    "    wg_length_in=y_length_in,\n",
    "    wg_length_out=y_length_out,\n",
    "    bend_length=y_length_bend,\n",
    "    direction=-1,\n",
    "    medium=si_non_perturb,\n",
    "    sidewall_angle=0,\n",
    ")\n",
    "\n",
    "# bottom arm\n",
    "bot_arm = make_waveguide(\n",
    "    x0=-mzi_length / 2,\n",
    "    y0=-wg_spacing / 2,\n",
    "    z0=0,\n",
    "    x1=mzi_length / 2,\n",
    "    y1=-wg_spacing / 2,\n",
    "    wg_width_0=w_core,\n",
    "    wg_width_1=w_core,\n",
    "    wg_thickness=h_core,\n",
    "    medium=si_non_perturb,\n",
    "    sidewall_angle=0,\n",
    ")\n",
    "\n",
    "# top arm: taper in\n",
    "taper_in = make_strip_rib_taper(\n",
    "    x0=-mzi_length / 2,\n",
    "    y0=wg_spacing / 2,\n",
    "    z0=0,\n",
    "    x1=-pin_length / 2,\n",
    "    y1=wg_spacing / 2,\n",
    "    core_width=w_core,\n",
    "    slab_width=2 * x_side,\n",
    "    core_thickness=h_core,\n",
    "    slab_thickness=h_slab,\n",
    "    medium=si_non_perturb,\n",
    "    sidewall_angle=0,\n",
    ")\n",
    "\n",
    "# top arm: PIN region\n",
    "pin_wg = make_rib_waveguide(\n",
    "    x0=-pin_length / 2,\n",
    "    y0=wg_spacing / 2,\n",
    "    z0=0,\n",
    "    x1=pin_length / 2,\n",
    "    y1=wg_spacing / 2,\n",
    "    core_width=w_core,\n",
    "    slab_width=2 * x_total,\n",
    "    side_width=x_total - x_side,\n",
    "    core_thickness=h_core,\n",
    "    slab_thickness=h_slab,\n",
    "    side_thickness=h_side,\n",
    "    medium=si_perturb,\n",
    "    sidewall_angle=0,\n",
    ")\n",
    "\n",
    "\n",
    "# top arm: taper out\n",
    "taper_out = make_strip_rib_taper(\n",
    "    x0=mzi_length / 2,\n",
    "    y0=wg_spacing / 2,\n",
    "    z0=0,\n",
    "    x1=pin_length / 2,\n",
    "    y1=wg_spacing / 2,\n",
    "    core_width=w_core,\n",
    "    slab_width=2 * x_side,\n",
    "    core_thickness=h_core,\n",
    "    slab_thickness=h_slab,\n",
    "    medium=si_non_perturb,\n",
    "    sidewall_angle=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f15010-bc50-4a64-b23f-def22f85e6ad",
   "metadata": {},
   "source": [
    "Before proceeding further into creating an optic simulation, we can combine created structures into a `Scene` object for visualization purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a72ac000-fa53-4a9d-b682-1881c9e4a1a8",
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     "shell.execute_reply": "2025-05-15T10:57:13.307081Z"
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   },
   "outputs": [
    {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "scene = td.Scene(\n",
    "    structures=[coupler_in, coupler_out, bot_arm] + taper_in + pin_wg + taper_out,\n",
    "    medium=sio2,\n",
    ")\n",
    "\n",
    "_, ax = plt.subplots(3, 1, figsize=(15, 7))\n",
    "scene.plot(z=z_core, ax=ax[0])\n",
    "scene.plot(z=z_slab, ax=ax[1])\n",
    "scene.plot(x=0, ax=ax[2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a4ae46-1e84-4b2a-bbe8-8daed9d45869",
   "metadata": {},
   "source": [
    "### Optic Sources and Monitors\n",
    "Having inspected the geometry we now continue to define other components of an optic simulation. First, we define several auxiliary variables corresponding to:\n",
    "- SiO2 buffer around the circuit,\n",
    "- reduction of simulation in x direction,\n",
    "- location and size of source injection and monitor ports,\n",
    "- width of the injected pulse, and\n",
    "- frequencies at which to measure output signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a98abfb7-e00d-4d01-9051-5705ce8a62c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.308824Z",
     "iopub.status.busy": "2025-05-15T10:57:13.308710Z",
     "iopub.status.idle": "2025-05-15T10:57:13.311526Z",
     "shell.execute_reply": "2025-05-15T10:57:13.311087Z"
    }
   },
   "outputs": [],
   "source": [
    "buffer = 1\n",
    "dx = 6\n",
    "\n",
    "port_x = mzi_length / 2 + splitter_length - dx - 1\n",
    "port_y = 0\n",
    "port_z = z_core\n",
    "\n",
    "port_size = (0, 3, 2)\n",
    "\n",
    "fwidth = freq0 / 5\n",
    "freqs = np.linspace(freq0 - fwidth / 10, freq0 + fwidth / 10, 201)\n",
    "wvls = td.C_0 / freqs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817262f8-b788-4a87-a3cf-b4beefee6e12",
   "metadata": {},
   "source": [
    "The propagation of EM pulse in the circuit is triggered by a `ModeSource`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "53daeaf9-aa2f-44d8-aec9-166b8f50944b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.312808Z",
     "iopub.status.busy": "2025-05-15T10:57:13.312696Z",
     "iopub.status.idle": "2025-05-15T10:57:13.314946Z",
     "shell.execute_reply": "2025-05-15T10:57:13.314669Z"
    }
   },
   "outputs": [],
   "source": [
    "src = td.ModeSource(\n",
    "    center=(-port_x - 0.5, port_y, port_z),\n",
    "    size=port_size,\n",
    "    direction=\"+\",\n",
    "    mode_index=0,\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e61e979-a80a-4cf2-a91d-c4a9dcc3c6a4",
   "metadata": {},
   "source": [
    "We will measure mode decompositions in the input port and two output ports. Additionally, we will sample field distribution on the horizontal plane passing through the circuit's center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e751a7ec-9637-418a-a486-35d19eff9d46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.316209Z",
     "iopub.status.busy": "2025-05-15T10:57:13.316072Z",
     "iopub.status.idle": "2025-05-15T10:57:13.319673Z",
     "shell.execute_reply": "2025-05-15T10:57:13.319141Z"
    }
   },
   "outputs": [],
   "source": [
    "mnt_in = td.ModeMonitor(\n",
    "    center=(-port_x, port_y, port_z),\n",
    "    size=port_size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=td.ModeSpec(num_modes=3),\n",
    "    name=\"in\",\n",
    ")\n",
    "mnt_out = td.ModeMonitor(\n",
    "    center=(port_x, port_y, port_z),\n",
    "    size=port_size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=td.ModeSpec(num_modes=3),\n",
    "    name=\"out\",\n",
    ")\n",
    "mnt_field = td.FieldMonitor(\n",
    "    center=(0, 0, z_core), size=(effective_inf, effective_inf, 0), freqs=[freq0], name=\"field\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3d1121-8b34-47f4-a4a2-5336137d8f01",
   "metadata": {},
   "source": [
    "### Simulation Assembly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eaa89603-1eb7-45f9-98ab-f0b21789fd71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.320926Z",
     "iopub.status.busy": "2025-05-15T10:57:13.320816Z",
     "iopub.status.idle": "2025-05-15T10:57:13.339672Z",
     "shell.execute_reply": "2025-05-15T10:57:13.339118Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_size = (scene.size[0] - 2 * dx, scene.size[1] + 2 * buffer, scene.size[2] + 2 * buffer)\n",
    "\n",
    "sim = td.Simulation.from_scene(\n",
    "    scene,\n",
    "    center=scene.center,\n",
    "    size=sim_size,\n",
    "    sources=[src],\n",
    "    monitors=[mnt_in, mnt_out, mnt_field],\n",
    "    run_time=6e-11,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64919785-1b3d-452c-95e9-2f0194af9d60",
   "metadata": {},
   "source": [
    "Let us perform another visual inspection to ensure the right placement of monitors and sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1d30133c-932c-4a7a-af98-30e586271dd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.340925Z",
     "iopub.status.busy": "2025-05-15T10:57:13.340779Z",
     "iopub.status.idle": "2025-05-15T10:57:13.517892Z",
     "shell.execute_reply": "2025-05-15T10:57:13.517379Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 1, figsize=(15, 7))\n",
    "sim.plot(x=sim.center[0], ax=ax[0])\n",
    "sim.plot(z=sim.center[2], ax=ax[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3574dff-8838-47c2-95b6-d77c6b24889f",
   "metadata": {},
   "source": [
    "This can also be done using a three dimensional plotting functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5922cc33-60d1-4ff4-9f76-af8b61222b7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.519754Z",
     "iopub.status.busy": "2025-05-15T10:57:13.519483Z",
     "iopub.status.idle": "2025-05-15T10:57:13.542679Z",
     "shell.execute_reply": "2025-05-15T10:57:13.542365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    
\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       ""
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "sim.plot_3d(width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279c2c81-6143-452a-86c9-622c8b09588c",
   "metadata": {},
   "source": [
    "### Applying Carrier Distributions\n",
    "At this point we have created an optic simulation that contains materials with charge perturbation models, however no electron and hole distributions have been provided to it. That is, when we submit such a simulation for solving, all mediums with perturbation models will be considered as regular materials with no perturbations applied. In our case, this corresponds to the case of undoped materials."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b361a1-4836-46de-beea-c75d948ee146",
   "metadata": {},
   "source": [
    "To obtain simulations in which perturbation models are sampled against provided carrier densities one can use a convenience method `perturbed_mediums_copy()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b9d51593-d6ca-4fdd-874b-7a93a3e620da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:13.544396Z",
     "iopub.status.busy": "2025-05-15T10:57:13.544281Z",
     "iopub.status.idle": "2025-05-15T10:57:39.739250Z",
     "shell.execute_reply": "2025-05-15T10:57:39.738828Z"
    }
   },
   "outputs": [],
   "source": [
    "def apply_charge(charge_data):\n",
    "    perturbed_sims = []\n",
    "    for n, v in enumerate(charge_data[\"charge_mnt\"].electrons.values.voltage.data):\n",
    "        e_data = charge_data[\"charge_mnt\"].electrons.sel(voltage=v)\n",
    "        h_data = charge_data[\"charge_mnt\"].holes.sel(voltage=v)\n",
    "        perturbed_sims.append(\n",
    "            sim.perturbed_mediums_copy(\n",
    "                electron_density=e_data,\n",
    "                hole_density=h_data,\n",
    "            )\n",
    "        )\n",
    "    return perturbed_sims\n",
    "\n",
    "\n",
    "perturbed_sims = apply_charge(charge_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a3a888-717f-46d3-b95c-3ab4295bb511",
   "metadata": {},
   "source": [
    "The difference in the permittivity values in the modulator cross-section compared to the undoped case can be visualized as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8dc9b2f3-c53b-4519-90cf-f8fb7f914b1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:39.740445Z",
     "iopub.status.busy": "2025-05-15T10:57:39.740352Z",
     "iopub.status.idle": "2025-05-15T10:57:39.932560Z",
     "shell.execute_reply": "2025-05-15T10:57:39.932285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 1, figsize=(10, 5))\n",
    "\n",
    "sampling_region = td.Box(center=(0, wg_spacing / 2, port_z), size=(0, 6, 2))\n",
    "eps_undoped = sim.epsilon(box=sampling_region).isel(x=0, drop=True)\n",
    "\n",
    "for ax_ind, ind in enumerate([5, 10]):\n",
    "    eps_doped = perturbed_sims[ind].epsilon(box=sampling_region).isel(x=0, drop=True)\n",
    "    eps_doped = eps_doped.interp(y=eps_undoped.y, z=eps_undoped.z)\n",
    "    eps_diff = np.abs(np.real(eps_doped - eps_undoped))\n",
    "    eps_diff.plot(x=\"y\", ax=ax[ax_ind])\n",
    "\n",
    "    ax[ax_ind].set_aspect(\"equal\")\n",
    "    ax[ax_ind].set_title(f\"Bias: {voltages[ind]:1.1f} V\")\n",
    "    ax[ax_ind].set_xlabel(\"y (um)\")\n",
    "    ax[ax_ind].set_ylabel(\"z (um)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633f182d-c9f9-452e-8b93-ec09f8c55beb",
   "metadata": {},
   "source": [
    "### Waveguide Mode Analysis\n",
    "\n",
    "Before proceeding to full-wave simulations one can investigate the influence of applied voltage on the propagation index of waveguide modes in the modulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d05b24db-4424-4847-abf4-f621ec84aa95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:39.933865Z",
     "iopub.status.busy": "2025-05-15T10:57:39.933752Z",
     "iopub.status.idle": "2025-05-15T10:57:39.935534Z",
     "shell.execute_reply": "2025-05-15T10:57:39.935306Z"
    }
   },
   "outputs": [],
   "source": [
    "from tidy3d.plugins.mode import ModeSolver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85989be-efd2-48c0-adf2-7d02f51770f3",
   "metadata": {},
   "source": [
    "Let us define a plane for which waveguide modes will be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f8597fa-7503-4228-bf91-2bb07b7cc7dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:39.936366Z",
     "iopub.status.busy": "2025-05-15T10:57:39.936261Z",
     "iopub.status.idle": "2025-05-15T10:57:39.995081Z",
     "shell.execute_reply": "2025-05-15T10:57:39.994723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "mode_plane = td.Box(center=(0, wg_spacing / 2, port_z), size=port_size)\n",
    "\n",
    "# visualize\n",
    "ax = sim.plot(x=mode_plane.center[0])\n",
    "mode_plane.plot(x=mode_plane.center[0], ax=ax, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1a8e73-1ba6-4622-9d66-587c49feb899",
   "metadata": {},
   "source": [
    "Create a mode solver specification for each carrier distribution. We will consider only the first mode at 11 different frequencies. Also, given that the anticipated changes are small, double precision is turned on for mode solving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc0ca9d9-a43f-40d7-aeb6-4f5f55bc82f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:39.996171Z",
     "iopub.status.busy": "2025-05-15T10:57:39.996092Z",
     "iopub.status.idle": "2025-05-15T10:57:40.010267Z",
     "shell.execute_reply": "2025-05-15T10:57:40.010067Z"
    }
   },
   "outputs": [],
   "source": [
    "mode_solvers = {}\n",
    "for n, psim in enumerate(perturbed_sims):\n",
    "    ms = ModeSolver(\n",
    "        simulation=psim,\n",
    "        plane=mode_plane,\n",
    "        freqs=np.linspace(freqs[0], freqs[-1], 11),\n",
    "        mode_spec=td.ModeSpec(num_modes=1, precision=\"double\"),\n",
    "    )\n",
    "    mode_solvers[f\"v={voltages[n]}\"] = ms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ec93af-bcae-4e1c-b8cf-e7651ddaf930",
   "metadata": {},
   "source": [
    "Perform calculation on our servers. Note that since the associated simulation objects contain custom medium data, they are automatically reduced to the mode solver plane for optimizing uploading/downloading data. Setting `reduce_simulation=True` will silence the associated warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dd7efd06-135c-49da-9902-e39d6a8b5702",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:57:40.011254Z",
     "iopub.status.busy": "2025-05-15T10:57:40.011181Z",
     "iopub.status.idle": "2025-05-15T11:00:40.155477Z",
     "shell.execute_reply": "2025-05-15T11:00:40.154873Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
v=0.0                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.1                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.2                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.30000000000000... → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.4                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.5                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.60000000000000... → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.70000000000000... → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.8                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=0.9                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=1.0                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=1.1                 → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "v=1.20000000000000... → success ━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:09\n",
       "
\n"
      ],
      "text/plain": [
       "v=0.0                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.1                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.2                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.30000000000000... → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.4                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.5                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.60000000000000... → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.70000000000000... → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.8                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=0.9                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=1.0                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=1.1                 → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n",
       "v=1.20000000000000... → \u001b[32msuccess\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[33m0:00:09\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
12:45:58 UTC Batch complete.                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:45:58 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "batch = web.Batch(simulations=mode_solvers, reduce_simulation=True)\n",
    "ms_data = batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16252a03-94ce-4a2c-8717-a9f50a5eeb09",
   "metadata": {},
   "source": [
    "Let us extract the effective propagation index for the central frequency and visualize its dependence on applied voltage. As expected, increasing the applied voltage results in a more pronounced change in the propagation index and, at the same time, in larger losses in the waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "839bf0fe-3346-479b-99f9-26f6c35b9375",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:00:40.513743Z",
     "iopub.status.busy": "2025-05-15T11:00:40.513683Z",
     "iopub.status.idle": "2025-05-15T11:00:43.613196Z",
     "shell.execute_reply": "2025-05-15T11:00:43.612986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "n_eff_freq0 = [md.n_complex.sel(f=freq0, mode_index=0).values for _, md in ms_data.items()]\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "ax[0].plot(voltages, np.real(n_eff_freq0), \".-\")\n",
    "\n",
    "ax[0].set_xlabel(\"Bias (V)\")\n",
    "ax[0].set_ylabel(\"Re[$n_{eff}$]\")\n",
    "\n",
    "ax[1].plot(voltages, np.imag(n_eff_freq0), \".-\")\n",
    "\n",
    "ax[1].set_xlabel(\"Bias (V)\")\n",
    "ax[1].set_ylabel(\"Im[$n_{eff}$]\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f072e22e-8855-44a7-8695-1c191529aa20",
   "metadata": {},
   "source": [
    "Using the obtained propagation index values we can compute the associated phase change and loss over the PIN section of the modulator at the central wavelength of 1.55 um. From this information we can estimate the bias $V_\\pi$ required for a phase shift of $\\pi$ to be around 0.95 V. In [Zhou Liang et al 2011 Chinese Phys. Lett. 28 074202](https://doi.org/10.1088/0256-307X/28/7/074202) the value of 1.15 V was obtained for a similar experimental setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2c96600b-7bba-4b99-a757-8acd5453a134",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-05-15T11:00:43.614280Z",
     "iopub.status.busy": "2025-05-15T11:00:43.614190Z",
     "iopub.status.idle": "2025-05-15T11:00:43.692860Z",
     "shell.execute_reply": "2025-05-15T11:00:43.692587Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "phase_shift = 2 * np.pi / wvl_um * (np.real(n_eff_freq0) - np.real(n_eff_freq0[0])) * pin_length\n",
    "intensity = np.exp(-4 * np.pi * np.imag(n_eff_freq0) * pin_length / wvl_um)\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "ax[0].plot(voltages, phase_shift / np.pi, \".-\")\n",
    "ax[0].axhline(y=-1, color=\"k\", linestyle=\"--\")\n",
    "\n",
    "ax[0].set_xlabel(\"Bias (V)\")\n",
    "ax[0].set_ylabel(r\"Phase shift ($\\pi$)\")\n",
    "\n",
    "ax[1].plot(voltages, 10 * np.log10(intensity), \".-\")\n",
    "\n",
    "ax[1].set_xlabel(\"Bias (V)\")\n",
    "ax[1].set_ylabel(\"Output power (dB)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437d4710-78dd-46ff-aa00-24b27726d236",
   "metadata": {},
   "source": [
    "### Full-wave simulation of the circuit\n",
    "\n",
    "**Note: the cost of running this section is over 15 FlexCredits.**\n",
    "\n",
    "While performing full-wave simulations is not the most cost effective approach for such a simple geometry, we still perform it here for zero and 1 V bias values for demonstration purposes. Alternatively, one could achieve the full-wave simulation accuracy by dividing the problem setup into smaller components and obtaining the S-matrix for each of them using Tidy3D's [ComponentModeler](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/) plugin. Here, we simply perform the simulation over the entire circuit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b4818e-512d-4e18-a9e2-0fda5f222e67",
   "metadata": {},
   "source": [
    "For a clearer demonstration let us obtain free carrier distribution for the approximately found $V_\\pi$ of 0.95 V."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8bcfe165-46c3-4d0e-9963-12138eca51f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:00:43.693915Z",
     "iopub.status.busy": "2025-05-15T11:00:43.693826Z",
     "iopub.status.idle": "2025-05-15T11:05:36.130303Z",
     "shell.execute_reply": "2025-05-15T11:05:36.130068Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
12:57:09 -03 WARNING: Structure at 'structures[0]' has bounds that extend       \n",
       "             exactly to simulation edges. This can cause unexpected behavior. If\n",
       "             intending to extend the structure to infinity along one dimension, \n",
       "             use td.inf as a size variable instead to make this explicit.       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:57:09 -03\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Suppressed 11 WARNING messages.                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m11\u001b[0m\u001b[31m WARNING messages.                           \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Created task 'mzi_pin_Vpi' with resource_id                        \n",
       "             'hec-7762da5e-63ef-4bb1-bc1a-105a390db78c' and task_type           \n",
       "             'HEAT_CHARGE'.                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mzi_pin_Vpi'\u001b[0m with resource_id                        \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-7762da5e-63ef-4bb1-bc1a-105a390db78c'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
       "             HeatCharge simulations is subject to change in the future.         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8aac1f8e94814a0098b4b1e2eab34640",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:57:12 -03 Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:57:12 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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": [
       "
12:57:15 -03 status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:57:15 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
       "             Terminating the Python script will not stop the job running on the \n",
       "             cloud.                                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85bd6087b49149f48207438e5d328935",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:57:54 -03 status = preprocess                                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:57:54 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:58:26 -03 starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:58:26 -03\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
12:58:27 -03 running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:58:27 -03\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:06:12 -03 status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:12 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9bed35c091984a7e8ea7a8aaaf30bd59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:06:16 -03 Loading simulation from charge_mzi_pin_Vpi.hdf5                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:16 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from charge_mzi_pin_Vpi.hdf5                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Structure at 'structures[0]' has bounds that extend       \n",
       "             exactly to simulation edges. This can cause unexpected behavior. If\n",
       "             intending to extend the structure to infinity along one dimension, \n",
       "             use td.inf as a size variable instead to make this explicit.       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Suppressed 11 WARNING messages.                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m11\u001b[0m\u001b[31m WARNING messages.                           \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Warning messages were found in the solver log. For more   \n",
       "             information, check 'SimulationData.log' or use                     \n",
       "             'web.download_log(task_id)'.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "bc_v1 = td.HeatChargeBoundarySpec(\n",
    "    condition=td.VoltageBC(source=td.DCVoltageSource(voltage=list(np.linspace(0, 0.95, 3)))),\n",
    "    placement=td.StructureBoundary(structure=contact_p.name),\n",
    ")\n",
    "\n",
    "boundary_conditions = [bc_v1, bc_v2]\n",
    "\n",
    "charge_sim_95 = charge_sim.updated_copy(boundary_spec=boundary_conditions)\n",
    "\n",
    "charge_data_95 = web.run(charge_sim_95, task_name=\"mzi_pin_Vpi\", path=\"charge_mzi_pin_Vpi.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "706bb455-e81d-4afd-b8fc-6b34c712a765",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:05:36.622577Z",
     "iopub.status.busy": "2025-05-15T11:05:36.622507Z",
     "iopub.status.idle": "2025-05-15T11:05:41.810801Z",
     "shell.execute_reply": "2025-05-15T11:05:41.810142Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "perturbed_sim_0_95V = apply_charge(charge_data_95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fdd30785-8514-49e8-bd70-7e9768c533e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:05:41.812261Z",
     "iopub.status.busy": "2025-05-15T11:05:41.812167Z",
     "iopub.status.idle": "2025-05-15T11:05:42.003898Z",
     "shell.execute_reply": "2025-05-15T11:05:42.003674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 1, figsize=(10, 5))\n",
    "\n",
    "sampling_region = td.Box(center=(0, wg_spacing / 2, port_z), size=(0, 6, 2))\n",
    "eps_undoped = sim.epsilon(box=sampling_region).isel(x=0, drop=True)\n",
    "\n",
    "for ax_ind, ind in enumerate([0, 2]):\n",
    "    eps_doped = perturbed_sim_0_95V[ind].epsilon(box=sampling_region).isel(x=0, drop=True)\n",
    "    eps_doped = eps_doped.interp(y=eps_undoped.y, z=eps_undoped.z)\n",
    "    eps_diff = np.abs(np.real(eps_doped - eps_undoped))\n",
    "    eps_diff.plot(x=\"y\", ax=ax[ax_ind])\n",
    "\n",
    "    ax[ax_ind].set_aspect(\"equal\")\n",
    "    ax[ax_ind].set_title(f\"Bias: {[0, 0.95][ax_ind]:1.2f} V\")\n",
    "    ax[ax_ind].set_xlabel(\"y (um)\")\n",
    "    ax[ax_ind].set_ylabel(\"z (um)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7596d156-99c8-4d32-9943-912c810e77de",
   "metadata": {},
   "source": [
    "For convenience, we use `Batch` functionality to submit two simulation for solving in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "504fdd7b-fffe-4987-bd95-d683be09514a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:05:42.004925Z",
     "iopub.status.busy": "2025-05-15T11:05:42.004817Z",
     "shell.execute_reply": "2025-05-15T11:21:24.799142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d174fd49b3a14dba9c3e0f41efa9c9a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:06:45 -03 Started working on Batch containing 2 tasks.                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:45 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:06:54 -03 Maximum FlexCredit cost: 27.249 for the whole batch.               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:06:54 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m27.249\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Use 'Batch.real_cost()' to get the billed FlexCredit cost after    \n",
       "             completion.                                                        \n",
       "
\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": "a32c08cba02845f18d1e5ac66d427f0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    },
    {
     "data": {
      "text/html": [
       "
13:17:37 -03 Batch complete.                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:17:37 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch = web.Batch(\n",
    "    simulations={\"Bias: 0 V\": perturbed_sims[0], \"Bias: 0.95 V\": perturbed_sim_0_95V[-1]}\n",
    ")\n",
    "batch_data = batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7865d9d1-7e82-48b7-bb04-4e6ba63cfe7b",
   "metadata": {},
   "source": [
    "Let us first visualize the field distribution across the whole circuit. Comparing the two simulations, one can qualitatively observe two effects:\n",
    "1) power in the output waveguide is drastically changed as bias value of 0.95 V is close to $V_\\pi$,\n",
    "2) the signal strength visibly decreases along the PIN section of the modulator in the case of 0.95 V bias value due to losses associated with the increased concentration of free carriers.\n",
    "\n",
    "Note that unequal aspect ratio is used for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0be60cc5-44d1-48ba-be56-8513c124c1e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:21:29.292236Z",
     "iopub.status.busy": "2025-05-15T11:21:29.291675Z",
     "iopub.status.idle": "2025-05-15T11:21:38.547682Z",
     "shell.execute_reply": "2025-05-15T11:21:38.546992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 1, figsize=(10, 10))\n",
    "\n",
    "for ind, (key, data) in enumerate(batch_data.items()):\n",
    "    batch_data[key].plot_field(\"field\", \"S\", ax=ax[ind])\n",
    "    ax[ind].set_title(key)\n",
    "    ax[ind].set_aspect(\"auto\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8688e81-72ee-4d52-8dac-647f91c54787",
   "metadata": {},
   "source": [
    "A more quantitative visualization of data can be obtained from the output `ModeMonitor` results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "93ad28f6-0c6e-4051-b587-0869e6f39d90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T11:21:38.548911Z",
     "iopub.status.busy": "2025-05-15T11:21:38.548796Z",
     "iopub.status.idle": "2025-05-15T11:21:43.153880Z",
     "shell.execute_reply": "2025-05-15T11:21:43.153674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(15, 4))\n",
    "for ind, (key, data) in enumerate(batch_data.items()):\n",
    "    ax.plot(wvls, batch_data[key][\"out\"].amps.sel(direction=\"+\", mode_index=0).abs ** 2)\n",
    "\n",
    "ax.set_title(\"Output Port\")\n",
    "ax.set_xlabel(\"Wavelength (um)\")\n",
    "ax.set_ylabel(\"Normalized transmission power\")\n",
    "ax.legend([\"0 V\", \"0.95 V\"])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "406079ff-dab6-4c50-8bfd-40bce97e1a65",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Active photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to simulate an electro-optic Mach-Zehnder modulator using Tidy3D and a third party charge solver.",
  "feature_image": "./img/MZI_modulator.png",
  "features": [
   "Mode analysis",
   "Charge",
   "Perturbation medium"
  ],
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Mach-Zehnder, MZI, MZM, waveguide, phase-shifter, electro-optic, charge, Tidy3D, free carrier, carrier injection, carrier depletion, 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": "Modeling electro-optic Mach-Zehnder modulator using Tidy3D | Flexcompute",
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