{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Silicon resonator benchmark \n",
    "\n",
    "In this example, we reproduce the findings of Zhang et al. (2018), which is linked [here](https://www.osapublishing.org/ol/abstract.cfm?uri=ol-43-8-1842).\n",
    "\n",
    "This notebook was originally developed and written by Romil Audhkhasi (USC). \n",
    "\n",
    "The paper investigates the resonances of silicon structures by measuring their transmission spectrum under varying geometric parameters.\n",
    "\n",
    "The paper uses a commercial finite element solver , which matches the result from Tidy3D.\n",
    "\n",
    "<img src=\"img/Si_struct.png\" alt=\"diagram\" width=\"500\"/>\n",
    "\n",
    "(Citation: Opt. Lett. 43, 1842-1845 (2018).  With permission from the Optical Society)\n",
    "\n",
    "To do this calculation, we use a broadband pulse and frequency monitor to measure the flux on the opposite side of the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# standard python imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# tidy3D import\n",
    "import tidy3d as td\n",
    "from tidy3d import web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nm = 1e-3\n",
    "\n",
    "# define the frequencies we want to measure\n",
    "Nfreq = 1000\n",
    "wavelengths = nm * np.linspace(1050, 1400, Nfreq)\n",
    "freqs = td.constants.C_0 / wavelengths\n",
    "\n",
    "# define the frequency center and width of our pulse\n",
    "freq0 = freqs[len(freqs) // 2]\n",
    "freqw = freqs[0] - freqs[-1]\n",
    "\n",
    "# Define material properties\n",
    "n_SiO2 = 1.46\n",
    "n_Si = 3.52\n",
    "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
    "Si = td.Medium(permittivity=n_Si**2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# space between resonators and source\n",
    "spc = 1.5\n",
    "\n",
    "# geometric parameters\n",
    "Px = Py = P = 650 * nm  # periodicity in x and y\n",
    "t = 260 * nm  # thickness of silcon\n",
    "g = 80 * nm  # gap size\n",
    "L = 480 * nm  # length in x\n",
    "w_sum = 400 * nm  # sum of lengths in y\n",
    "\n",
    "# resolution (should be commensurate with periodicity)\n",
    "dl = P / 32\n",
    "\n",
    "# computes widths in y (w1 and w2) given the difference in lengths in y and the sum of lengths\n",
    "def calc_ws(delta):\n",
    "    \"\"\"delta is a tunable parameter used to break symmetry.\n",
    "    w_sum = w1 + w2\n",
    "    delta = w1 - w2\n",
    "    w_sum + delta = 2 * w1\n",
    "    \"\"\"\n",
    "    w1 = (w_sum + delta) / 2\n",
    "    w2 = w_sum - w1\n",
    "    return w1, w2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# total size in z and [x,y,z]\n",
    "Lz = spc + t + t + spc\n",
    "sim_size = [Px, Py, Lz]\n",
    "\n",
    "# sio2 substrate\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, 0, -Lz / 2],\n",
    "        size=[td.inf, td.inf, 2 * (spc + t)],\n",
    "    ),\n",
    "    medium=SiO2,\n",
    "    name=\"substrate\",\n",
    ")\n",
    "\n",
    "# creates a list of structures given a value of 'delta'\n",
    "def geometry(delta):\n",
    "    w1, w2 = calc_ws(delta)\n",
    "    center_y = (w1 - w2) / 2.0\n",
    "\n",
    "    cell1 = td.Structure(\n",
    "        geometry=td.Box(\n",
    "            center=[0, center_y + (g + w1) / 2.0, t / 2.0],\n",
    "            size=[L, w1, t],\n",
    "        ),\n",
    "        medium=Si,\n",
    "        name=\"cell1\",\n",
    "    )\n",
    "\n",
    "    cell2 = td.Structure(\n",
    "        geometry=td.Box(\n",
    "            center=[0, center_y - (g + w2) / 2.0, t / 2.0],\n",
    "            size=[L, w2, t],\n",
    "        ),\n",
    "        medium=Si,\n",
    "        name=\"cell2\",\n",
    "    )\n",
    "\n",
    "    return [substrate, cell1, cell2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# time dependence of source\n",
    "gaussian = td.GaussianPulse(freq0=freq0, fwidth=freqw)\n",
    "\n",
    "# plane wave source\n",
    "source = td.PlaneWave(\n",
    "    source_time=gaussian,\n",
    "    direction=\"-\",\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, Lz / 2 - spc + 2 * dl),\n",
    "    pol_angle=0.0,\n",
    ")\n",
    "\n",
    "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
    "run_time = 7e-12\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# monitor fields on other side of structure (substrate side) at range of frequencies\n",
    "monitor = td.FluxMonitor(\n",
    "    center=[0.0, 0.0, -Lz / 2 + spc - 2 * dl],\n",
    "    size=[td.inf, td.inf, 0],\n",
    "    freqs=freqs,\n",
    "    name=\"flux\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Case Studies\n",
    "\n",
    "Here we define the three simulations to run\n",
    "\n",
    "- With no resonators (normalization)\n",
    "- With symmetric (delta = 0) resonators\n",
    "- With asymmetric (delta != 0) resonators\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_spec = td.GridSpec(\n",
    "    grid_x=td.UniformGrid(dl=dl),\n",
    "    grid_y=td.UniformGrid(dl=dl),\n",
    "    grid_z=td.AutoGrid(min_steps_per_wvl=32),\n",
    ")\n",
    "\n",
    "# normalizing run (no Si) to get baseline transmission vs freq\n",
    "# can be run for shorter time as there are no resonances\n",
    "sim_empty = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=[substrate],\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time / 10,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")\n",
    "\n",
    "# run with delta = 0\n",
    "sim_d0 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=geometry(0),\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")\n",
    "\n",
    "# run with delta = 20nm\n",
    "sim_d20 = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=grid_spec,\n",
    "    structures=geometry(20 * nm),\n",
    "    sources=[source],\n",
    "    monitors=[monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Structure visualization in various planes\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 8))\n",
    "sim_d0.plot_eps(x=0, ax=ax1)\n",
    "sim_d0.plot_eps(y=g, ax=ax2)\n",
    "sim_d0.plot_eps(z=0, ax=ax3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "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\">[15:47:31] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'normalization'</span> with task_id <span style=\"color: #008000; text-decoration-color: #008000\">'fdve-6a422d36-5e38-4669-abfb-769a9b70055dv1'</span>.   <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">139</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[15:47:31]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'normalization'\u001b[0m with task_id \u001b[32m'fdve-6a422d36-5e38-4669-abfb-769a9b70055dv1'\u001b[0m.   \u001b]8;id=142576;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=723244;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-8461f5b2-464f-40ac-9976-24e1c24129e6v1'</span>.                                             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
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       "\u001b[2;36m[15:47:34]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Si-resonator-delta-0'\u001b[0m with task_id                                           \u001b]8;id=255417;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=425947;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-8461f5b2-464f-40ac-9976-24e1c24129e6v1'\u001b[0m.                                             \u001b[2m             \u001b[0m\n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-2d8bf75d-4a40-4301-a794-b452e8cb64d1v1'</span>.                                             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
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       "\u001b[2;36m[15:47:36]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Si-resonator-delta-20'\u001b[0m with task_id                                          \u001b]8;id=157761;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=621995;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-2d8bf75d-4a40-4301-a794-b452e8cb64d1v1'\u001b[0m.                                             \u001b[2m             \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "</pre>\n"
      ],
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       "\n"
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     "metadata": {},
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    },
    {
     "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\">[15:47:39] </span>Started working on Batch.                                                               <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#402\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">402</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[15:47:39]\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                                               \u001b]8;id=22639;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=833141;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#402\u001b\\\u001b[2m402\u001b[0m\u001b]8;;\u001b\\\n"
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       "Output()"
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     },
     "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\">[15:50:20] </span>Batch complete.                                                                         <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">container.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#436\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">436</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[15:50:20]\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                                         \u001b]8;id=79132;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py\u001b\\\u001b[2mcontainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=788144;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/container.py#436\u001b\\\u001b[2m436\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch = web.Batch(\n",
    "    simulations={\n",
    "        \"normalization\": sim_empty,\n",
    "        \"Si-resonator-delta-0\": sim_d0,\n",
    "        \"Si-resonator-delta-20\": sim_d20,\n",
    "    },\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "results = batch.run(path_dir=\"data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Results and Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "Output()"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      ],
      "text/plain": [
       "\n"
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    {
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       "</pre>\n"
      ],
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       "\u001b[2;36m[15:50:23]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/fdve-\u001b[93m6a422d36-5e38-4669-abfb-769a9b70055d\u001b[0mv1.hdf5          \u001b]8;id=592309;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=860858;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
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       "\n"
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     },
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    },
    {
     "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\">[15:50:24] </span>loading SimulationData from data/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">8461f5b2-464f-40ac-9976-24e1c24129e6</span>v1.hdf5          <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">512</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[15:50:24]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/fdve-\u001b[93m8461f5b2-464f-40ac-9976-24e1c24129e6\u001b[0mv1.hdf5          \u001b]8;id=458445;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=664412;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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       "Output()"
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    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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      ],
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       "\n"
      ]
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    },
    {
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       "<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\">[15:50:25] </span>loading SimulationData from data/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">2d8bf75d-4a40-4301-a794-b452e8cb64d1</span>v1.hdf5          <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">512</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[15:50:25]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/fdve-\u001b[93m2d8bf75d-4a40-4301-a794-b452e8cb64d1\u001b[0mv1.hdf5          \u001b]8;id=44573;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=526054;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#512\u001b\\\u001b[2m512\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\">[15:50:25] </span>WARNING: Simulation final field decay value of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000136</span> is greater than the simulation shutoff <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/log.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">log.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/log.py#50\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">50</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>threshold of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>. Consider simulation again with large run_time duration for more accurate   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">         </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>results.                                                                                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">         </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[15:50:25]\u001b[0m\u001b[2;36m \u001b[0mWARNING: Simulation final field decay value of \u001b[1;36m0.000136\u001b[0m is greater than the simulation shutoff \u001b]8;id=966988;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/log.py\u001b\\\u001b[2mlog.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=288417;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/log.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mthreshold of \u001b[1;36m1e-05\u001b[0m. Consider simulation again with large run_time duration for more accurate   \u001b[2m         \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mresults.                                                                                       \u001b[2m         \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_data = batch.load(path_dir=\"data\")\n",
    "flux_norm = batch_data[\"normalization\"][\"flux\"].flux\n",
    "trans_g0 = batch_data[\"Si-resonator-delta-0\"][\"flux\"].flux / flux_norm\n",
    "trans_g20 = batch_data[\"Si-resonator-delta-20\"][\"flux\"].flux / flux_norm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normalizing run computes the transmitted flux for an air -> SiO2 interface, which is just below unity due to some reflection.\n",
    "\n",
    "While not technically necessary for this example, since this transmission can be computed analytically, it is often a good idea to run a normalizing run so you can accurately measure the *change* in output when the structure is added.  For example, for multilayer structures, the normalizing run displays frequency dependence, which would make it prudent to include in the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot transmission, compare to paper results, look similar\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
    "wavelengths_nm = td.C_0 / trans_g0.f / nm\n",
    "plt.plot(wavelengths_nm, trans_g0.values, color=\"red\", label=\"$\\delta=0$\")\n",
    "plt.plot(wavelengths_nm, trans_g20.values, color=\"blue\", label=\"$\\delta=20~nm$\")\n",
    "plt.xlabel(\"wavelength ($nm$)\")\n",
    "plt.ylabel(\"Transmission\")\n",
    "plt.xlim([1050, 1400])\n",
    "plt.ylim([0, 1])\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Comparison\n",
    "\n",
    "Compare this plot to published results:\n",
    "\n",
    "<img src=\"img/Si_plot.png\" alt=\"diagram\" width=\"400\"/>\n",
    "\n",
    "(Citation: Opt. Lett. 43, 1842-1845 (2018).  With permission from the Optical Society)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "13181b6ba8ff496bb01cd4687b3709f2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
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