{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20a4e5dc-3039-4bc6-b926-0787e8905cc8",
   "metadata": {},
   "source": [
    "# RF electrode in a microring modulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453eb413-f00b-4d6b-ae30-4118f6f5d5da",
   "metadata": {},
   "source": [
    "The microring modulator (MRR) is a key component in modern photonics design. In this notebook, we model the RF electrode in a silicon-on-insulator MRR, which carries the electrical signal responsible for optical modulation.\n",
    "\n",
    "In a full fidelity model, the MRR would require multiphysics coupling of thermal, charge, optical, and RF simulations. For this RF-only demonstration, we will approximate the reverse bias p-n junction, responsible for the optical phase modulation, with a lumped RC element. The focus will be on minimizing return loss within the operational bandwidth. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5268fdc-8831-4024-a5d4-af5d2588a634",
   "metadata": {},
   "source": [
    "<center><img src=\"img/mrr-electrode-render.png\" width=640 /></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4cadb679-bbec-4382-820d-447730755145",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdstk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.rf as rf\n",
    "from tidy3d import web\n",
    "\n",
    "td.config.logging.level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa00f082-fd09-4184-b571-d8a0fd10215d",
   "metadata": {},
   "source": [
    "## Building the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe75d52-5508-434b-8609-72a8b33114a3",
   "metadata": {},
   "source": [
    "### Key Parameters and Materials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8010c791-fdee-45be-8d5b-5f192534ced4",
   "metadata": {},
   "source": [
    "The bandwidth of the simulation is 10-100 GHz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "205624a8-15b1-4155-bd6c-5712d16fa0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequency bandwidth\n",
    "f_min, f_max = (10e9, 100e9)\n",
    "f0 = (f_max + f_min) / 2\n",
    "freqs = np.linspace(f_min, f_max, 101)\n",
    "\n",
    "# Dimensions\n",
    "len_inf = 1e5\n",
    "bounds_electrode = (2.62, 3.02)\n",
    "bounds_oxide = (-3.017, 2.62)\n",
    "bounds_sub = (-len_inf, -3.017)\n",
    "TM = bounds_electrode[1] - bounds_electrode[0]  # electrode thickness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa925522-ebb7-46a7-843f-e9f2b4a7a43c",
   "metadata": {},
   "source": [
    "The equivalent RC parameters of the reverse bias p-n junction can be estimated using a lumped circuit model. Details of the calculation are presented in section 2.2.3 of Ref [1]. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08e3837f-e8d2-4ff8-9d39-31849fe8fba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lumped electrical parameters\n",
    "C_lumped = 17.325e-15  # Farads\n",
    "R_lumped = 113.12  # Ohms\n",
    "Zref = 50  # Ohms, port reference impedance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087b2b99-742c-4f2b-ad8c-62fbd62f4c58",
   "metadata": {},
   "source": [
    "The relevant materials are defined below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83a812a6-72c3-4cb4-8b1d-ee407f82e4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mediums\n",
    "med_sub = td.Medium(permittivity=12.3)  # Si substrate\n",
    "med_oxide = td.Medium(permittivity=4.2)  # SiO2\n",
    "med_metal = rf.LossyMetalMedium(conductivity=17, frequency_range=(f_min, f_max))  # Metal [S/um]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d898a0-5e24-44d0-aa30-77d582c4a7cf",
   "metadata": {},
   "source": [
    "### Geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac61f97-17dd-4bb3-b524-f87a4f3999ff",
   "metadata": {},
   "source": [
    "In lieu of building the electrode geometry from scratch, we will import a pre-constructed 2D shape from GDS. It is then extruded to the appropriate thickness. For more details on working with GDS files, see this [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSImport/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf229754-c040-458c-9dd0-ba85b7bc6029",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load GDS and build metal electrode geometry\n",
    "gds_mmr = gdstk.read_gds(\"misc/mrr-electrode.gds\")\n",
    "g_electrode = td.Geometry.from_gds(\n",
    "    gds_mmr[\"MRR\"], gds_layer=12, gds_dtype=0, slab_bounds=bounds_electrode, axis=2\n",
    ")\n",
    "\n",
    "# Dielectric layers\n",
    "g_oxide = td.Box.from_bounds(\n",
    "    rmin=(-len_inf, -len_inf, bounds_oxide[0]), rmax=(len_inf, len_inf, bounds_oxide[1])\n",
    ")\n",
    "g_sub = td.Box.from_bounds(\n",
    "    rmin=(-len_inf, -len_inf, bounds_sub[0]), rmax=(len_inf, len_inf, bounds_sub[1])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bc10a3-8bfd-419e-ab25-cb3d10a4b068",
   "metadata": {},
   "source": [
    "The geometries are combined with the corresponding mediums into `Structure` instances below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62ae14f9-e553-44c5-b34a-7ef774fbde22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Structures\n",
    "str_sub = td.Structure(geometry=g_sub, medium=med_sub)\n",
    "str_oxide = td.Structure(geometry=g_oxide, medium=med_oxide)\n",
    "str_electrode = td.Structure(geometry=g_electrode, medium=med_metal)\n",
    "structure_list = [str_sub, str_oxide, str_electrode]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87597206-e661-42ab-8fbc-d578caac1381",
   "metadata": {},
   "source": [
    "### Grid and Boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1977037f-4d55-47cb-b162-48951c804dc8",
   "metadata": {},
   "source": [
    "The external boundaries of the simulation are terminated with Perfectly Matched Layers (PMLs) by default. We add some space padding around the electrode, although we do not expect it to radiate significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a02a7a97-61ea-4085-8834-0c3d72be05a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation center and size\n",
    "sim_X0, sim_Y0, sim_Z0 = (30, -30, 0)\n",
    "sim_LX, sim_LY, sim_LZ = (450, 300, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796f2a5b-5602-43cd-abdc-e07d05485bf8",
   "metadata": {},
   "source": [
    "The grid should be able to resolve key features in the electrode, such as the gap and corners, for accurate results. To this end, we use the `LayerRefinementSpec` feature to automatically analyze the electrode geometry and generate an appropriately fine mesh. For added fidelity, we also define a `MeshOverrideStructure` around the microring region that constrains maximum grid size in the in-plane directions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68da361d-3108-4016-8203-cf6e87abddff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Layer refinement\n",
    "lr1 = rf.LayerRefinementSpec.from_structures(\n",
    "    structures=[str_electrode],\n",
    "    corner_refinement=td.GridRefinement(dl=1, num_cells=2),\n",
    "    min_steps_along_axis=1,\n",
    ")\n",
    "\n",
    "# Mesh override\n",
    "refine_box = td.MeshOverrideStructure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(16.5, 1, bounds_electrode[0]), rmax=(43.5, 27, bounds_electrode[1])\n",
    "    ),\n",
    "    dl=(1, 1, None),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2dfccae-3a46-4c77-a137-1239496ddbcd",
   "metadata": {},
   "source": [
    "The overall grid specification is defined below. The rest of the simulation grid is automatically generated based on the shortest wavelength. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "706563fd-a0bc-4634-9676-ca329b11abda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid specification\n",
    "grid_spec = td.GridSpec.auto(\n",
    "    min_steps_per_wvl=12,\n",
    "    wavelength=td.C_0 / f_max,\n",
    "    layer_refinement_specs=[lr1],\n",
    "    override_structures=[refine_box],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0648b289-dd11-4dc4-aee0-cfeafa7805e0",
   "metadata": {},
   "source": [
    "### Monitors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bf4ee0-bc84-44a3-8177-ae01fe6ebe6b",
   "metadata": {},
   "source": [
    "We define a field monitor just below the electrode plane for visualization purposes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0929e71-dbfc-432c-b389-5000056055e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Field Monitor\n",
    "mon_1 = td.FieldMonitor(\n",
    "    center=(sim_X0, sim_Y0, bounds_electrode[0]),\n",
    "    size=(len_inf, len_inf, 0),\n",
    "    freqs=[f_min, f0, f_max],\n",
    "    name=\"field in-plane\",\n",
    ")\n",
    "\n",
    "monitor_list = [mon_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9f2275-72e3-47c8-8db3-6f4657d86dbe",
   "metadata": {},
   "source": [
    "### Lumped Ports and Elements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b96aa02-9013-4fea-a774-2e8aa0c471ba",
   "metadata": {},
   "source": [
    "We will use lumped ports and elements in this model since the structure is extremely sub-wavelength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "687693ed-1d24-4aac-8c0b-028076abc001",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lumped port\n",
    "LP1 = rf.LumpedPort(\n",
    "    center=(30, -85, bounds_electrode[1]),\n",
    "    size=(60, 10, 0),\n",
    "    voltage_axis=1,\n",
    "    name=\"LP1\",\n",
    "    impedance=Zref,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2733757-5b47-41f5-b555-d59a88759e23",
   "metadata": {},
   "source": [
    "The equivalent RC circuit representing the p-n junction is defined below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2096e13-ff3a-41c8-a860-263ce6aac082",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lumped element (RC network)\n",
    "lumped_network = rf.RLCNetwork(resistance=R_lumped, capacitance=C_lumped, network_topology=\"series\")\n",
    "LE1 = rf.LinearLumpedElement(\n",
    "    center=(30, 24.795, bounds_electrode[1]),\n",
    "    size=(2, 2.2, 0),\n",
    "    voltage_axis=1,\n",
    "    network=lumped_network,\n",
    "    name=\"LumpedRC\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53719a8-4ee4-4df9-b7e6-db8b16dd2076",
   "metadata": {},
   "source": [
    "### Simulation and TerminalComponentModeler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf132a48-6896-4687-aa53-2cfe09bb2660",
   "metadata": {},
   "source": [
    "The simulation and `TerminalComponentModeler` instances are defined below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b0672cf-ba91-47f5-8438-799ea092f80c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = td.Simulation(\n",
    "    center=(sim_X0, sim_Y0, sim_Z0),\n",
    "    size=(sim_LX, sim_LY, sim_LZ),\n",
    "    grid_spec=grid_spec,\n",
    "    structures=structure_list,\n",
    "    monitors=monitor_list,\n",
    "    lumped_elements=[LE1],\n",
    "    run_time=1e-10,\n",
    ")\n",
    "\n",
    "tcm = rf.TerminalComponentModeler(\n",
    "    simulation=sim,\n",
    "    ports=[LP1],\n",
    "    freqs=freqs,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cfd6cc-89c5-433b-b2bd-55eaaf39acbc",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbdddae8-82ca-470f-986c-a84ae7bb5254",
   "metadata": {},
   "source": [
    "Before running the simulation, let us visualize the geometry and grid. The lumped port (green) and lumped RC element (blue) are also depicted below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "531a4d12-6a59-4fc8-b85c-f64b8a6705a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Metal electrode plane\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), tight_layout=True)\n",
    "tcm.simulation.plot_grid(z=bounds_electrode[1], ax=ax[0])\n",
    "tcm.plot_sim(z=bounds_electrode[1], ax=ax[0], monitor_alpha=0, hlim=(-150, 210), vlim=(-125, 50))\n",
    "tcm.simulation.plot_grid(z=bounds_electrode[0], ax=ax[1])\n",
    "tcm.plot_sim(z=bounds_electrode[1], ax=ax[1], monitor_alpha=0, hlim=(15, 45), vlim=(0, 30))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0630c2-7590-485d-a288-60b71ce3ecbf",
   "metadata": {},
   "source": [
    "We can also use the 3D viewer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9d4bfee1-5acb-4b93-81e8-930913a42c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a2124d-49bf-409c-bd91-b6e692d4c541",
   "metadata": {},
   "source": [
    "## Running the Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c2e1b6-a1cd-4b03-a2ea-56b9906217d3",
   "metadata": {},
   "source": [
    "The simulation is executed below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "428696d7-dc1d-4566-9c55-c027a9476eeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:29:00 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'microring_electrode'</span> with resource_id                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-6857c885-b3a5-4037-9712-a7d128ef7ec5'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:29:00 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'microring_electrode'\u001b[0m with resource_id                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-6857c885-b3a5-4037-9712-a7d128ef7ec5'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5b-1574ec339f7e'</span></a>.                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=984133;https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=150179;https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=984133;https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=584908;https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=984133;https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\u001b\\\u001b[32m-f69572f7-2ab1-4c96-bd\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=984133;https://tidy3d.simulation.cloud/rf?taskId=pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e\u001b\\\u001b[32m5b-1574ec339f7e'\u001b[0m\u001b]8;;\u001b\\.                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=152427;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b664041b0a934ee7be547dca497f3da8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:29:08 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.113</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:29:08 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.113\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 after run.         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Subtasks status - microring_electrode                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mSubtasks status - microring_electrode                              \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-f69572f7-2ab1-4c96-bd5b-1574ec339f7e'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43a5b46fda5c46bdb047364ad022c0ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = preprocess                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:29:15 EST </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:29:15 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:49:14 EST </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:49:14 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:49:27 EST </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:49:27 EST\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.059</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.059\u001b[0m.                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a83c577d3c8497985aee7c5460c3d0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">15:49:29 EST </span>Loading component modeler data from data/microring_electrode.hdf5  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:49:29 EST\u001b[0m\u001b[2;36m \u001b[0mLoading component modeler data from data/microring_electrode.hdf5  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tcm_data = web.run(tcm, task_name=\"microring_electrode\", path=\"data/microring_electrode.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c226f3-a9d2-4c62-85ee-0481c23a4be3",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b82750-771a-4558-8532-d9514233d5cd",
   "metadata": {},
   "source": [
    "### S-parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ea978b-41ae-46d1-abba-283dd4f924c2",
   "metadata": {},
   "source": [
    "We extract S11 below. For comparison purposes, we also import benchmark data from a commercial FEM solver based on a similar setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd91cbf6-f784-4a15-aabd-2f482e99d8f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract S11\n",
    "smat = tcm_data.smatrix().data\n",
    "S11 = np.conjugate(smat.squeeze())\n",
    "\n",
    "# Import benchmark data\n",
    "freqs_ben, S11dB_ben = np.genfromtxt(\n",
    "    \"misc/mrr_fem_sparam.csv\", delimiter=\",\", skip_header=1, unpack=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9fd4c9-b7d5-4af5-8e5b-689e5c4b66e3",
   "metadata": {},
   "source": [
    "The return loss (RL) is shown below. We find very good agreement with the benchmark. From the RL plot, we observe that the electrode has a -3 dB bandwidth of up to 55 GHz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d73baeff-9d31-4b94-8327-801674d50435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot return loss\n",
    "fig, ax = plt.subplots(figsize=(10, 4), tight_layout=True)\n",
    "ax.plot(freqs / 1e9, 20 * np.log10(np.abs(S11)), label=\"RF solver\")\n",
    "ax.plot(freqs_ben, S11dB_ben, ls=\"--\", color=\"gray\", label=\"Other FEM\")\n",
    "ax.set_title(\"Return Loss (dB)\")\n",
    "ax.set_ylabel(\"dB\")\n",
    "ax.set_xlabel(\"f (GHz)\")\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36a08f1-5c99-4020-95ec-9d1f240e3e4b",
   "metadata": {},
   "source": [
    "### Field profile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f7fd03-4248-4a6d-b0d8-67c7b443fd2b",
   "metadata": {},
   "source": [
    "We extract the field monitor data and plot the in-plane field magnitude at the center frequency below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "261e3372-b855-49c6-b0ba-ab56336a2650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract monitor data\n",
    "sim_data = tcm_data.data[\"LP1\"]\n",
    "\n",
    "# Plot field monitor data\n",
    "fig, ax = plt.subplots(figsize=(10, 5), tight_layout=True)\n",
    "f_plot = f0\n",
    "sim_data.plot_field(\"field in-plane\", field_name=\"E\", val=\"abs\", f=f_plot, ax=ax)\n",
    "ax.set_xlim(-150, 210)\n",
    "ax.set_ylim(-150, 80)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b06531-fd4a-43c6-aff0-b15eaa6a0161",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2326bc-953f-4524-a12e-f493424accbb",
   "metadata": {},
   "source": [
    "[1] Wang, Zhao. \"Silicon micro-ring resonator modulator for inter/intra-data centre applications.\" PhD diss., 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0c94ede-0540-4710-88ff-e0e6f7a9b770",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Microwave and RF devices"
  ],
  "description": "In this notebook, we demonstrate how to simulate a RF electrode in a microring resonator. Results are compared against benchmark.",
  "feature_image": "./img/mrr-electrode-render.png",
  "features": [
   "Lumped port",
   "Smatrix plugin"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "microring resonator, RF photonics, lumped port, lumped element, RF electrode, silicon-on-insulator, RC circuit",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  },
  "title": "RF electrode in a microring modulator"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}