{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Resonator benchmark (COMSOL)\n", "\n", "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FHighQ_Si.ipynb).\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 finite element solver (COMSOL), which matches the result from Tidy3D.\n", "\n", "\"diagram\"\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": { "execution": { "iopub.execute_input": "2022-07-21T21:05:14.089221Z", "iopub.status.busy": "2022-07-21T21:05:14.088661Z", "iopub.status.idle": "2022-07-21T21:05:15.058272Z", "shell.execute_reply": "2022-07-21T21:05:15.057806Z" }, "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up Simulation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.060236Z", "iopub.status.busy": "2022-07-21T21:05:15.060082Z", "iopub.status.idle": "2022-07-21T21:05:15.063035Z", "shell.execute_reply": "2022-07-21T21:05:15.062781Z" }, "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)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.064378Z", "iopub.status.busy": "2022-07-21T21:05:15.064194Z", "iopub.status.idle": "2022-07-21T21:05:15.066543Z", "shell.execute_reply": "2022-07-21T21:05:15.066317Z" }, "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" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.068003Z", "iopub.status.busy": "2022-07-21T21:05:15.067881Z", "iopub.status.idle": "2022-07-21T21:05:15.071164Z", "shell.execute_reply": "2022-07-21T21:05:15.070836Z" }, "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., t/2.],\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., t/2.],\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": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.072452Z", "iopub.status.busy": "2022-07-21T21:05:15.072348Z", "iopub.status.idle": "2022-07-21T21:05:15.074926Z", "shell.execute_reply": "2022-07-21T21:05:15.074545Z" }, "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", "# Simulation run time. Note you need to run a long time to calculate high Q resonances.\n", "run_time = 7e-12" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.076241Z", "iopub.status.busy": "2022-07-21T21:05:15.076122Z", "iopub.status.idle": "2022-07-21T21:05:15.078298Z", "shell.execute_reply": "2022-07-21T21:05:15.078060Z" }, "tags": [] }, "outputs": [], "source": [ "# monitor fields on other side of structure (substrate side) at range of frequencies\n", "monitor = td.FluxMonitor(\n", " center=[0., 0., -Lz/2 + spc - 2 * dl],\n", " size=[td.inf, td.inf, 0],\n", " freqs=freqs,\n", " name='flux')" ] }, { "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": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.079640Z", "iopub.status.busy": "2022-07-21T21:05:15.079515Z", "iopub.status.idle": "2022-07-21T21:05:15.085613Z", "shell.execute_reply": "2022-07-21T21:05:15.085395Z" }, "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(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.pml(z=True))\n", "\n", "# run with delta = 0\n", "sim_d0 = td.Simulation(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.pml(z=True))\n", "\n", "# run with delta = 20nm\n", "sim_d20 = td.Simulation(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.pml(z=True))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T21:05:15.086953Z", "iopub.status.busy": "2022-07-21T21:05:15.086832Z", "iopub.status.idle": "2022-07-21T21:05:15.458377Z", "shell.execute_reply": "2022-07-21T21:05:15.457921Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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   "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",
    ")\n",
    "\n",
    "results = batch.run(path_dir='data')"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Results and Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-21T21:06:46.977107Z",
     "iopub.status.busy": "2022-07-21T21:06:46.976971Z",
     "iopub.status.idle": "2022-07-21T21:06:52.423141Z",
     "shell.execute_reply": "2022-07-21T21:06:52.422755Z"
    }
   },
   "outputs": [
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       "\u001b[2;36m           \u001b[0m         simulation again with large run_time duration for more      \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m         accurate results.                                           \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']\n",
    "trans_g0 = batch_data['Si-resonator-delta-0']['flux'] / flux_norm\n",
    "trans_g20 = batch_data['Si-resonator-delta-20']['flux'] / flux_norm"
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2022-07-21T21:06:52.440051Z",
     "iopub.status.busy": "2022-07-21T21:06:52.439863Z",
     "iopub.status.idle": "2022-07-21T21:06:52.549509Z",
     "shell.execute_reply": "2022-07-21T21:06:52.549076Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
<Figure size 432x324 with 1 Axes>\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x324 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Comparison\n",
    "\n",
    "Compare this plot to published results computed using COMSOL (FEM):\n",
    "\n",
    "\"diagram\"\n",
    "\n",
    "(Citation: Opt. Lett. 43, 1842-1845 (2018).  With permission from the Optical Society)"
   ]
  }
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↑ simulation.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 40.2/40.2 kB • ? • 0:00:00\n
\n",
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