{
"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",
"
[19:44:37] WARNING Simulation final field decay value of 0.00167 is greater than the simulation webapi.py:421\n",
" shutoff threshold of 1e-05. Consider simulation again with large run_time \n",
" duration for more accurate results. \n",
"
\n"
],
"text/plain": [
"\u001b[2;36m[19:44:37]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Simulation final field decay value of \u001b[1;36m0.00167\u001b[0m is greater than the simulation \u001b]8;id=21177;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=900227;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#421\u001b\\\u001b[2m421\u001b[0m\u001b]8;;\u001b\\\n",
"\u001b[2;36m \u001b[0m shutoff threshold of \u001b[1;36m1e-05\u001b[0m. Consider simulation again with large run_time \u001b[2m \u001b[0m\n",
"\u001b[2;36m \u001b[0m duration for more 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\"].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": {
"execution": {
"iopub.execute_input": "2023-02-03T01:44:37.053482Z",
"iopub.status.busy": "2023-02-03T01:44:37.053368Z",
"iopub.status.idle": "2023-02-03T01:44:37.173781Z",
"shell.execute_reply": "2023-02-03T01:44:37.173352Z"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"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 computed using COMSOL (FEM):\n",
"\n",
"\n",
"\n",
"(Citation: Opt. Lett. 43, 1842-1845 (2018). With permission from the Optical Society)"
]
}
],
"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": {
"079d9be595a0482da482f0da0a28600f": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/output",
"_model_module_version": "1.0.0",
"_model_name": "OutputModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/output",
"_view_module_version": "1.0.0",
"_view_name": "OutputView",
"layout": "IPY_MODEL_b8b877c162594288baf6478aab78972b",
"msg_id": "",
"outputs": [
{
"data": {
"text/html": "
normalization: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━100%0:00:00\nSi-resonator-delta-0: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━100%0:00:00\nSi-resonator-delta-20: status = postprocess ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 71%-:--:--\nnormalization: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━100%0:00:00\nSi-resonator-delta-0: status = success ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━100%0:00:00\nSi-resonator-delta-20: status = postprocess ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━ 71%-:--:--
![\"diagram\"](\"img/Si_struct.png\")
\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": [
"![\"diagram\"](\"img/Si_plot.png\")
\n",
"\n",
"(Citation: Opt. Lett. 43, 1842-1845 (2018). With permission from the Optical Society)"
]
}
],
"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": {
"079d9be595a0482da482f0da0a28600f": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/output",
"_model_module_version": "1.0.0",
"_model_name": "OutputModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/output",
"_view_module_version": "1.0.0",
"_view_name": "OutputView",
"layout": "IPY_MODEL_b8b877c162594288baf6478aab78972b",
"msg_id": "",
"outputs": [
{
"data": {
"text/html": "