{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Resonator benchmark (COMSOL)\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", "\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": "2023-02-03T01:42:34.225677Z", "iopub.status.busy": "2023-02-03T01:42:34.225317Z", "iopub.status.idle": "2023-02-03T01:42:35.273133Z", "shell.execute_reply": "2023-02-03T01:42:35.272729Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[19:42:35] WARNING This version of Tidy3D was pip installed from the 'tidy3d-beta' repository on __init__.py:103\n", " PyPI. Future releases will be uploaded to the 'tidy3d' repository. From now on, \n", " please use 'pip install tidy3d' instead. \n", "\n" ], "text/plain": [ "\u001b[2;36m[19:42:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on \u001b]8;id=709222;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=992054;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m please use \u001b[32m'pip install tidy3d'\u001b[0m instead. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
INFO Using client version: 1.9.0rc1 __init__.py:121\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1 \u001b]8;id=667022;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=268547;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "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": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.275523Z", "iopub.status.busy": "2023-02-03T01:42:35.275242Z", "iopub.status.idle": "2023-02-03T01:42:35.278795Z", "shell.execute_reply": "2023-02-03T01:42:35.278411Z" }, "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": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.280937Z", "iopub.status.busy": "2023-02-03T01:42:35.280792Z", "iopub.status.idle": "2023-02-03T01:42:35.284128Z", "shell.execute_reply": "2023-02-03T01:42:35.283783Z" }, "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": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.286027Z", "iopub.status.busy": "2023-02-03T01:42:35.285876Z", "iopub.status.idle": "2023-02-03T01:42:35.290366Z", "shell.execute_reply": "2023-02-03T01:42:35.289999Z" }, "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": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.292231Z", "iopub.status.busy": "2023-02-03T01:42:35.292107Z", "iopub.status.idle": "2023-02-03T01:42:35.295119Z", "shell.execute_reply": "2023-02-03T01:42:35.294754Z" }, "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": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.297109Z", "iopub.status.busy": "2023-02-03T01:42:35.296960Z", "iopub.status.idle": "2023-02-03T01:42:35.299756Z", "shell.execute_reply": "2023-02-03T01:42:35.299388Z" }, "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": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.301777Z", "iopub.status.busy": "2023-02-03T01:42:35.301626Z", "iopub.status.idle": "2023-02-03T01:42:35.310341Z", "shell.execute_reply": "2023-02-03T01:42:35.309985Z" }, "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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()),\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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()),\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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()),\n", ")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T01:42:35.312290Z", "iopub.status.busy": "2023-02-03T01:42:35.312138Z", "iopub.status.idle": "2023-02-03T01:42:35.805764Z", "shell.execute_reply": "2023-02-03T01:42:35.805310Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
INFO Auto meshing using wavelength 1.2252 defined from sources. grid_spec.py:510\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Auto meshing using wavelength \u001b[1;36m1.2252\u001b[0m defined from sources. \u001b]8;id=375732;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=619379;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#510\u001b\\\u001b[2m510\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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