"
]
},
"jetTransient": {
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sim_best = make_sim_params(param_history[-1], include_field_mnt=True)\n",
"ax = sim_best.plot(z=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "841a07a1-0a3b-47ce-b08c-a82495c1f04d",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-16T12:38:30.378251Z",
"start_time": "2026-03-16T12:37:24.767927Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"13:37:25 CET Created task 'taper final' with resource_id \n",
" 'fdve-a88910ab-50bb-46f1-9a37-f0658dc89367' and task_type 'FDTD' . \n",
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a88910ab-50b b-46f1-9a37-f0658dc89367' Task folder: 'default' 13:37:52 CET Estimated FlexCredit cost: 0.025 . Minimum cost depends on task \n",
" execution details. Use 'web.real_cost(task_id)' to get the billed \n",
" FlexCredit cost after a simulation run. \n",
"13:37:54 CET status = queued \n",
" To cancel the simulation, use 'web.abort(task_id)' or \n",
" 'web.delete(task_id)' or abort/delete the task in the web UI. \n",
" Terminating the Python script will not stop the job running on the \n",
" cloud. \n",
"13:38:20 CET starting up solver \n",
"13:38:21 CET running solver \n",
"13:38:22 CET early shutoff detected at 48 %, exiting. \n",
"13:38:23 CET status = success \n",
" View simulation result at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a88910ab-50b b-46f1-9a37-f0658dc89367' . \n",
"13:38:30 CET Loading results from simulation_data.hdf5 \n",
""
]
},
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],
"source": [
"f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, tight_layout=True, figsize=(11, 7))\n",
"\n",
"# plot original\n",
"sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax1)\n",
"sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax2)\n",
"\n",
"# plot optimized\n",
"sim_data_best.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax3)\n",
"sim_data_best.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax4)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "f028eb77-cb49-4ca4-8659-e9cd157a8724",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-16T12:38:31.702060Z",
"start_time": "2026-03-16T12:38:31.697503Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transmission improved from 72.19% to 99.77%\n"
]
}
],
"source": [
"transmission_start = float(measure_transmission(sim_data))\n",
"transmission_end = float(measure_transmission(sim_data_best))\n",
"print(\n",
" f\"Transmission improved from {(transmission_start * 100):.2f}% to {(transmission_end * 100):.2f}%\"\n",
")"
]
}
],
"metadata": {
"applications": [
"Passive photonic integrated circuit components"
],
"description": "This notebook demonstrates how to perform inverse design optimization of a waveguide taper using boundary gradients in Tidy3D FDTD.",
"feature_image": "./img/adjoint_5.png",
"features": [
"Adjoint inverse design",
"2D simulation"
],
"kernelspec": {
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"language": "python",
"name": "python3"
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"keywords": "boundary gradients, adjoint optimization, inverse design, Tidy3D, FDTD",
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"text/html": "↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.8/3.8 kB • ? • 0:00:00 \n↓ jax_sim_vjp.hdf5 ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 15.5/15.5 kB • ? • 0:00:00 \n↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 3.8/3.8 kB • ? • 0:00:00 \n ![\"Schematic](\"img/adjoint_5.png\")
\n",
"\n",
"We start by importing our typical python packages, plus `tidy3d` and `autograd`."
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "5cc1f2bd-ace7-449f-8260-926f76d0ea53",
"metadata": {
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"end_time": "2026-03-16T11:22:23.698770Z",
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},
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"source": [
"import autograd as ag\n",
"import autograd.numpy as anp\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"import tidy3d as td\n",
"import tidy3d.web as web"
]
},
{
"cell_type": "markdown",
"id": "6c95884d-328d-4834-8869-ec50262ce306",
"metadata": {},
"source": [
"## Set up\n",
"\n",
"Next we will define some basic parameters of the waveguide, such as the input and output waveguide dimensions, taper width, and taper length."
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "e6f5cf37-ac64-4ab3-9659-6ad92994a22c",
"metadata": {
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"end_time": "2026-03-16T11:22:23.715147Z",
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},
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"source": [
"wavelength = 1.0\n",
"freq0 = td.C_0 / wavelength\n",
"\n",
"wg_width_in = 0.5 * wavelength\n",
"wg_width_out = 5.0 * wavelength\n",
"\n",
"wg_medium = td.material_library[\"Si3N4\"][\"Philipp1973Sellmeier\"]\n",
"\n",
"wg_length = 1 * wavelength\n",
"taper_length = 10.0\n",
"\n",
"spc_pml = 1.5 * wavelength\n",
"\n",
"Lx = wg_length + taper_length + wg_length\n",
"Ly = spc_pml + max(wg_width_in, wg_width_out) + spc_pml"
]
},
{
"cell_type": "markdown",
"id": "d31afd9e-2b8c-4205-859e-a0c6ca476937",
"metadata": {},
"source": [
"Our taper is defined as a set of `num_points` connected vertices in a polygon. We define the fixed x positions of each vertex and then construct the y positions for the starting device (linear taper)."
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "2e069d59-6040-476b-92cb-46520184c156",
"metadata": {
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"source": [
"num_points = 101\n",
"x_start = -taper_length / 2\n",
"x_end = +taper_length / 2\n",
"xs = np.linspace(x_start, x_end, num_points)\n",
"\n",
"ys0 = (wg_width_in + (wg_width_out - wg_width_in) * (xs - x_start) / (x_end - x_start)) / 2.0"
]
},
{
"cell_type": "markdown",
"id": "88500eef-64a9-491c-8131-ff41ccf442ff",
"metadata": {},
"source": [
"Let's plot these points to make sure they seem reasonable."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "65d8bf65-53c7-4820-b396-cc9e7a19b72b",
"metadata": {
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",
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