{
"cells": [
{
"cell_type": "markdown",
"id": "fd61ded8",
"metadata": {},
"source": [
"# Plasmonic waveguide sensor for carbon dioxide detection\n",
"\n",
"**Note: the cost of running the entire notebook is about 10 FlexCredits.**\n",
"\n",
"Dielectric waveguides made of materials like silicon and other semiconductors have been well-known in integrated photonics to guide light with low loss. Plasmonic waveguides made of metals like silver and gold are another promising platform for nanoscale photonic applications relying on the use of surface plasmon polaritons (SPPs) to strongly confine and guide light. This tight field confinement leads to enhanced light-matter interactions, opening up applications in sensing, nanolasing, and quantum optics. Plasmonic waveguides such as metal-insulator-metal, insulator-metal-insulator, and metallic nanowires exhibit deep-subwavelength optical modes but also suffer from intrinsic losses due to metal absorption. Nonetheless, the combination of strong field confinement and sensitivity to surface conditions provides the potential for integrated nanophotonic devices and sensors based on plasmonic waveguides.\n",
"\n",
"In this notebook, we model a plasmonic sensor for CO$_2$ concentration detection. The sensor is based on a metal-insulator-metal (MIM) waveguide coupled to a square ring cavity. The square ring cavity is filled with a polymer called polyhexamethylene biguanide (PHMB), which is sensitive to CO$_2$. When CO$_2$ is present, it causes a change in the refractive index of the PHMB, which shifts the resonant wavelength of the sensor. This shift can be used to detect and quantify the CO$_2$ concentration over a range of 0 - 524 ppm, which covers atmospheric concentrations relevant to climate change monitoring. The design is based on the work `S. N. Khonina, N. L. Kazanskiy, M. A. Butt, A. Kaźmierczak, and R. Piramidowicz, \"Plasmonic sensor based on metal-insulator-metal waveguide square ring cavity filled with functional material for the detection of CO2 gas,\" Opt. Express 29, 16584-16594 (2021)` [DOI:10.1364/OE.423141](https://doi.org/10.1364/OE.423141).\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "166f2821",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"from tidy3d.plugins.mode import ModeSolver"
]
},
{
"cell_type": "markdown",
"id": "505789ba",
"metadata": {},
"source": [
"## Simulation Setup "
]
},
{
"cell_type": "markdown",
"id": "dd1266b6",
"metadata": {},
"source": [
"Define the simulation wavelength range to be 1.1 μm to 1.6 μm."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bffdac0b",
"metadata": {},
"outputs": [],
"source": [
"lda0 = 1.35 # central wavelength\n",
"freq0 = td.C_0 / lda0 # central frequency\n",
"ldas = np.linspace(1.1, 1.6, 301) # wavelength range\n",
"freqs = td.C_0 / ldas # frequency range\n",
"fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the source frequency"
]
},
{
"cell_type": "markdown",
"id": "ab29227b",
"metadata": {},
"source": [
"Four materials will be used in the simulation: gold, SiO$_2$, air, and the polymer PHMB. Gold is given by the [Drude](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Drude.html) model with the parameters taken from the [reference](https://doi.org/10.1364/OE.423141). SiO$_2$ is directly defined from the [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html). Air has a constant permittivity of 1. PHMB has a refractive index sensitive to the CO$_2$ concentration, which will be investigated in a parameter sweep, so we will define the PHMB medium later."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "72a4fdbe",
"metadata": {},
"outputs": [],
"source": [
"# define gold medium\n",
"au = td.Drude(eps_inf=9.0685, coeffs=[(135.44e14 / (2 * np.pi), 1.15e14 / (2 * np.pi))])\n",
"\n",
"# define sio2 medium\n",
"sio2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]\n",
"\n",
"# define air medium\n",
"air = td.Medium(permittivity=1)"
]
},
{
"cell_type": "markdown",
"id": "78842d57",
"metadata": {},
"source": [
"Define the geometric parameters of the sensor. These values have been optimized in the [reference](https://doi.org/10.1364/OE.423141). If further optimization is needed, [parameter sweeps](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/) or [adjoint optimization](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd1Intro/) can be applied."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "51d97221",
"metadata": {},
"outputs": [],
"source": [
"L = 0.6 # side length of the square cavity\n",
"g = 0.01 # gap size between the bus waveguide and the square cavity\n",
"W1 = 0.09 # width of the square cavity waveguide\n",
"W = 0.06 # width of the bus waveguide\n",
"H = 0.7 # thickness of the gold layer\n",
"inf_eff = 1e2 # effective infinity of the simulation"
]
},
{
"cell_type": "markdown",
"id": "1a11c6b6",
"metadata": {},
"source": [
"Define the structures of the sensor, which consists of the SiO$_2$ substrate, a gold layer, a bus waveguide filled with air, and a square ring cavity. The ring cavity is filled with PHMB. Therefore, we will define an outer square with PHMB and an inner square with gold. The outer square will be defined later with the PHMB medium.\n",
"\n",
"In addition, we define a mesh override structure around the bus waveguide and square cavity such that the grid will be refined in this region. Since we want to accurately capture the resonance of the device, using a fine grid is important. The refractive index of the mesh override structure is set to 20."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f0641bc2",
"metadata": {},
"outputs": [],
"source": [
"# define the substrate structure\n",
"substrate = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
" medium=sio2,\n",
")\n",
"\n",
"# define the gold layer structure on top of the substrate\n",
"au_layer = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, H)), medium=au\n",
")\n",
"\n",
"# define the air-filled bus waveguide structure\n",
"bus_waveguide = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-W / 2, -inf_eff, 0), rmax=(W / 2, inf_eff, H)), medium=air\n",
")\n",
"\n",
"# define the inner square cavity structure\n",
"ring_cavity_inner = td.Structure(\n",
" geometry=td.Box(center=(W / 2 + g + L / 2, 0, H / 2), size=(L - 2 * W1, L - 2 * W1, H)),\n",
" medium=au,\n",
")\n",
"\n",
"# define the region where grid will be refined\n",
"refine_structure = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-W / 2, -L / 2, 0), rmax=(W / 2 + g + L, L / 2, H)),\n",
" medium=td.Medium(permittivity=20**2),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "97eb660c",
"metadata": {},
"source": [
"Define simulation domain size, run time, source, and monitors. Since the plasmonic waveguide sensor has a small footprint and the field is mostly confined inside the waveguide, the simulation domain only needs to be slightly larger than the device itself. \n",
"\n",
"To excite the bus waveguide, a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) is used, similar to simulating a dielectric waveguide. A [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) is defined at the other end of the bus waveguide to compute transmission. To visualize the field distribution, we also add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) at `z=H/2`. This monitor will record the fields at two frequencies, the minimum frequency and the central frequency. The minimum frequency is off-resonant and the central frequency is close to being on-resonant."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f032e6eb",
"metadata": {},
"outputs": [],
"source": [
"buffer = lda0 / 2 # buffer length in each direction\n",
"\n",
"# define simulation domain size\n",
"Lx = L + buffer\n",
"Ly = L + g + W + buffer\n",
"Lz = H + buffer\n",
"\n",
"run_time = 1.5e-13 # simulation run time\n",
"\n",
"# add a mode source as excitation\n",
"mode_spec = td.ModeSpec(num_modes=1, target_neff=2)\n",
"mode_source = td.ModeSource(\n",
" center=(0, -L / 2 - lda0 / 4, H / 2),\n",
" size=(4 * W, 0, 3 * H),\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" direction=\"+\",\n",
" mode_spec=mode_spec,\n",
" mode_index=0,\n",
" num_freqs=5,\n",
")\n",
"\n",
"# add a mode monitor to measure transmission at the output waveguide\n",
"mode_monitor = td.ModeMonitor(\n",
" center=(0, L / 2 + lda0 / 4, H / 2),\n",
" size=mode_source.size,\n",
" freqs=freqs,\n",
" mode_spec=mode_spec,\n",
" name=\"mode\",\n",
")\n",
"\n",
"# add a field monitor to visualize field distribution at z=H/2\n",
"field_monitor = td.FieldMonitor(\n",
" center=(0, 0, H / 2), size=(td.inf, td.inf, 0), freqs=[np.min(freqs), freq0], name=\"field\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "353bf103",
"metadata": {},
"source": [
"To facilitate a parameter sweep to simulate different CO$_2$ concentrations, we will define a function `make_sim(n_phmb)`, which returns a `Tidy3D` [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) given the refractive index of PHMB. Since the simulation domain is small, if we use the PML boundary condition, we will receive warnings about structures being too close to the boundary. To avoid this warning, we use the [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html) boundary condition, which will work perfectly fine in this case. PML should be placed sufficiently far from any structures since evanescent field leaking into PML could cause the simulation to diverge. [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html), on the other hand, does not have this concern."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d5c67214",
"metadata": {},
"outputs": [],
"source": [
"def make_sim(n_phmb):\n",
" # define the phmb medium\n",
" phmb = td.Medium(permittivity=n_phmb**2)\n",
"\n",
" # define the outer square cavity structure\n",
" ring_cavity_outer = td.Structure(\n",
" geometry=td.Box(center=(W / 2 + g + L / 2, 0, H / 2), size=(L, L, H)), medium=phmb\n",
" )\n",
"\n",
" # define simulation\n",
" sim = td.Simulation(\n",
" center=(W / 2 + g + L / 2, 0, H / 2),\n",
" size=(Lx, Ly, Lz),\n",
" grid_spec=td.GridSpec.auto(\n",
" min_steps_per_wvl=30, wavelength=lda0, override_structures=[refine_structure]\n",
" ),\n",
" structures=[substrate, au_layer, bus_waveguide, ring_cavity_outer, ring_cavity_inner],\n",
" sources=[mode_source],\n",
" monitors=[mode_monitor, field_monitor],\n",
" run_time=run_time,\n",
" boundary_spec=td.BoundarySpec.all_sides(boundary=td.Absorber()),\n",
" )\n",
" return sim"
]
},
{
"cell_type": "markdown",
"id": "f7fc4df4",
"metadata": {},
"source": [
"To test our `make_sim` function, we define a simulation and visualize it."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "71beb4be",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sim = make_sim(1.55)\n",
"sim.plot(z=H / 2)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "397388ca",
"metadata": {},
"source": [
"We further visualize the grid around the cavity region to ensure it's sufficiently fine."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0d1ea62e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sim.plot(z=H / 2)\n",
"sim.plot_grid(z=H / 2, ax=ax)\n",
"ax.set_xlim(-W / 2, W / 2 + g + W1 + L)\n",
"ax.set_ylim(-L / 2, L / 2)\n",
"ax.set_aspect(\"auto\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e42bd64b",
"metadata": {},
"source": [
"Before running the simulations, we can use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) plugin to visualize the source mode to ensure we are launching the plasmonic waveguide mode. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c44e7177",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
12:46:41 CEST WARNING: Use the remote mode solver with subpixel averaging for \n",
"better accuracy through 'tidy3d.web.run(...)' or the deprecated \n",
"'tidy3d.plugins.mode.web.run(...)'. \n",
"
\n"
],
"text/plain": [
"\u001b[2;36m12:46:41 CEST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel averaging for \u001b[0m\n",
"\u001b[2;36m \u001b[0m\u001b[31mbetter accuracy through \u001b[0m\u001b[32m'tidy3d.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m or the deprecated \u001b[0m\n",
"\u001b[2;36m \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m. \u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mode_solver = ModeSolver(\n",
" simulation=sim,\n",
" plane=td.Box(center=mode_source.center, size=mode_source.size),\n",
" mode_spec=mode_spec,\n",
" freqs=[freq0],\n",
")\n",
"mode_data = mode_solver.solve()"
]
},
{
"cell_type": "markdown",
"id": "3deabd7f",
"metadata": {},
"source": [
"After running the solver, we can visualize the mode field. Indeed we see the plasmonic waveguide mode with the field concentrated inside the bus waveguide."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9a50f4e6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mode_data.intensity.plot(x=\"x\", y=\"z\", cmap=\"magma\", vmin=0, vmax=3e4)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "1d30cd66",
"metadata": {},
"source": [
"Lastly, we can estimate the cost of running this single simulation, which will give us a better idea of the cost of running a batch of simulations."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "af6d76e4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
12:50:20 CEST Created task 'plasmonic_sensor' with task_id \n",
"'fdve-4a1e666a-62bd-4d32-9765-6e626f8a60f6' and task_type 'FDTD'. \n",
"
12:50:23 CEST Maximum FlexCredit cost: 2.701. 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",
"
\n"
],
"text/plain": [
"\u001b[2;36m12:50:23 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m2.701\u001b[0m. Minimum cost depends on task \n",
"\u001b[2;36m \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
"\u001b[2;36m \u001b[0mFlexCredit cost after a simulation run. \n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
12:50:24 CEST Maximum FlexCredit cost: 2.701. 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",
"
\n"
],
"text/plain": [
"\u001b[2;36m12:50:24 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m2.701\u001b[0m. Minimum cost depends on task \n",
"\u001b[2;36m \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
"\u001b[2;36m \u001b[0mFlexCredit cost after a simulation run. \n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"job = web.Job(simulation=sim, task_name=\"plasmonic_sensor\")\n",
"estimated_cost = web.estimate_cost(job.task_id)"
]
},
{
"cell_type": "markdown",
"id": "1d682ca7",
"metadata": {},
"source": [
"## Running the Parameter Sweep "
]
},
{
"cell_type": "markdown",
"id": "f294f415",
"metadata": {},
"source": [
"Now that everything is set up correctly, we are ready to run a parameter sweep to investigate the transmission spectra at different PHMB refractive index values corresponding to different CO$_2$ concentrations. For the concentration in the range of 0 ppm to 524 ppm, the refractive index varies linearly from 1.55 to 1.48."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a28aa72a",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d5decea17f8442b3abaf1f425445bda5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
12:50:28 CEST Started working on Batch containing 4 tasks. \n",
"
![\"schematic](\"img/mim_co2_sensor.png\")
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "166f2821",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"from tidy3d.plugins.mode import ModeSolver"
]
},
{
"cell_type": "markdown",
"id": "505789ba",
"metadata": {},
"source": [
"## Simulation Setup "
]
},
{
"cell_type": "markdown",
"id": "dd1266b6",
"metadata": {},
"source": [
"Define the simulation wavelength range to be 1.1 μm to 1.6 μm."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bffdac0b",
"metadata": {},
"outputs": [],
"source": [
"lda0 = 1.35 # central wavelength\n",
"freq0 = td.C_0 / lda0 # central frequency\n",
"ldas = np.linspace(1.1, 1.6, 301) # wavelength range\n",
"freqs = td.C_0 / ldas # frequency range\n",
"fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the source frequency"
]
},
{
"cell_type": "markdown",
"id": "ab29227b",
"metadata": {},
"source": [
"Four materials will be used in the simulation: gold, SiO$_2$, air, and the polymer PHMB. Gold is given by the [Drude](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Drude.html) model with the parameters taken from the [reference](https://doi.org/10.1364/OE.423141). SiO$_2$ is directly defined from the [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html). Air has a constant permittivity of 1. PHMB has a refractive index sensitive to the CO$_2$ concentration, which will be investigated in a parameter sweep, so we will define the PHMB medium later."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "72a4fdbe",
"metadata": {},
"outputs": [],
"source": [
"# define gold medium\n",
"au = td.Drude(eps_inf=9.0685, coeffs=[(135.44e14 / (2 * np.pi), 1.15e14 / (2 * np.pi))])\n",
"\n",
"# define sio2 medium\n",
"sio2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]\n",
"\n",
"# define air medium\n",
"air = td.Medium(permittivity=1)"
]
},
{
"cell_type": "markdown",
"id": "78842d57",
"metadata": {},
"source": [
"Define the geometric parameters of the sensor. These values have been optimized in the [reference](https://doi.org/10.1364/OE.423141). If further optimization is needed, [parameter sweeps](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/) or [adjoint optimization](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd1Intro/) can be applied."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "51d97221",
"metadata": {},
"outputs": [],
"source": [
"L = 0.6 # side length of the square cavity\n",
"g = 0.01 # gap size between the bus waveguide and the square cavity\n",
"W1 = 0.09 # width of the square cavity waveguide\n",
"W = 0.06 # width of the bus waveguide\n",
"H = 0.7 # thickness of the gold layer\n",
"inf_eff = 1e2 # effective infinity of the simulation"
]
},
{
"cell_type": "markdown",
"id": "1a11c6b6",
"metadata": {},
"source": [
"Define the structures of the sensor, which consists of the SiO$_2$ substrate, a gold layer, a bus waveguide filled with air, and a square ring cavity. The ring cavity is filled with PHMB. Therefore, we will define an outer square with PHMB and an inner square with gold. The outer square will be defined later with the PHMB medium.\n",
"\n",
"In addition, we define a mesh override structure around the bus waveguide and square cavity such that the grid will be refined in this region. Since we want to accurately capture the resonance of the device, using a fine grid is important. The refractive index of the mesh override structure is set to 20."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f0641bc2",
"metadata": {},
"outputs": [],
"source": [
"# define the substrate structure\n",
"substrate = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
" medium=sio2,\n",
")\n",
"\n",
"# define the gold layer structure on top of the substrate\n",
"au_layer = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, H)), medium=au\n",
")\n",
"\n",
"# define the air-filled bus waveguide structure\n",
"bus_waveguide = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-W / 2, -inf_eff, 0), rmax=(W / 2, inf_eff, H)), medium=air\n",
")\n",
"\n",
"# define the inner square cavity structure\n",
"ring_cavity_inner = td.Structure(\n",
" geometry=td.Box(center=(W / 2 + g + L / 2, 0, H / 2), size=(L - 2 * W1, L - 2 * W1, H)),\n",
" medium=au,\n",
")\n",
"\n",
"# define the region where grid will be refined\n",
"refine_structure = td.Structure(\n",
" geometry=td.Box.from_bounds(rmin=(-W / 2, -L / 2, 0), rmax=(W / 2 + g + L, L / 2, H)),\n",
" medium=td.Medium(permittivity=20**2),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "97eb660c",
"metadata": {},
"source": [
"Define simulation domain size, run time, source, and monitors. Since the plasmonic waveguide sensor has a small footprint and the field is mostly confined inside the waveguide, the simulation domain only needs to be slightly larger than the device itself. \n",
"\n",
"To excite the bus waveguide, a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) is used, similar to simulating a dielectric waveguide. A [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) is defined at the other end of the bus waveguide to compute transmission. To visualize the field distribution, we also add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) at `z=H/2`. This monitor will record the fields at two frequencies, the minimum frequency and the central frequency. The minimum frequency is off-resonant and the central frequency is close to being on-resonant."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f032e6eb",
"metadata": {},
"outputs": [],
"source": [
"buffer = lda0 / 2 # buffer length in each direction\n",
"\n",
"# define simulation domain size\n",
"Lx = L + buffer\n",
"Ly = L + g + W + buffer\n",
"Lz = H + buffer\n",
"\n",
"run_time = 1.5e-13 # simulation run time\n",
"\n",
"# add a mode source as excitation\n",
"mode_spec = td.ModeSpec(num_modes=1, target_neff=2)\n",
"mode_source = td.ModeSource(\n",
" center=(0, -L / 2 - lda0 / 4, H / 2),\n",
" size=(4 * W, 0, 3 * H),\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" direction=\"+\",\n",
" mode_spec=mode_spec,\n",
" mode_index=0,\n",
" num_freqs=5,\n",
")\n",
"\n",
"# add a mode monitor to measure transmission at the output waveguide\n",
"mode_monitor = td.ModeMonitor(\n",
" center=(0, L / 2 + lda0 / 4, H / 2),\n",
" size=mode_source.size,\n",
" freqs=freqs,\n",
" mode_spec=mode_spec,\n",
" name=\"mode\",\n",
")\n",
"\n",
"# add a field monitor to visualize field distribution at z=H/2\n",
"field_monitor = td.FieldMonitor(\n",
" center=(0, 0, H / 2), size=(td.inf, td.inf, 0), freqs=[np.min(freqs), freq0], name=\"field\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "353bf103",
"metadata": {},
"source": [
"To facilitate a parameter sweep to simulate different CO$_2$ concentrations, we will define a function `make_sim(n_phmb)`, which returns a `Tidy3D` [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) given the refractive index of PHMB. Since the simulation domain is small, if we use the PML boundary condition, we will receive warnings about structures being too close to the boundary. To avoid this warning, we use the [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html) boundary condition, which will work perfectly fine in this case. PML should be placed sufficiently far from any structures since evanescent field leaking into PML could cause the simulation to diverge. [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html), on the other hand, does not have this concern."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d5c67214",
"metadata": {},
"outputs": [],
"source": [
"def make_sim(n_phmb):\n",
" # define the phmb medium\n",
" phmb = td.Medium(permittivity=n_phmb**2)\n",
"\n",
" # define the outer square cavity structure\n",
" ring_cavity_outer = td.Structure(\n",
" geometry=td.Box(center=(W / 2 + g + L / 2, 0, H / 2), size=(L, L, H)), medium=phmb\n",
" )\n",
"\n",
" # define simulation\n",
" sim = td.Simulation(\n",
" center=(W / 2 + g + L / 2, 0, H / 2),\n",
" size=(Lx, Ly, Lz),\n",
" grid_spec=td.GridSpec.auto(\n",
" min_steps_per_wvl=30, wavelength=lda0, override_structures=[refine_structure]\n",
" ),\n",
" structures=[substrate, au_layer, bus_waveguide, ring_cavity_outer, ring_cavity_inner],\n",
" sources=[mode_source],\n",
" monitors=[mode_monitor, field_monitor],\n",
" run_time=run_time,\n",
" boundary_spec=td.BoundarySpec.all_sides(boundary=td.Absorber()),\n",
" )\n",
" return sim"
]
},
{
"cell_type": "markdown",
"id": "f7fc4df4",
"metadata": {},
"source": [
"To test our `make_sim` function, we define a simulation and visualize it."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "71beb4be",
"metadata": {},
"outputs": [
{
"data": {
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",
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