{ "cells": [ { "cell_type": "markdown", "id": "f524391f", "metadata": {}, "source": [ "# Microwave frequency selective surface" ] }, { "cell_type": "markdown", "id": "73a4ff08", "metadata": {}, "source": [ "A Frequency Selective Surface (FSS) is a type of electromagnetic structure that allows certain frequencies to pass through while reflecting or blocking other frequencies. It is composed of a periodic array of conductive or dielectric elements that are spaced apart at a distance that is much smaller than the wavelength of the electromagnetic radiation. The FSS can be designed to selectively filter and manipulate the electromagnetic waves that pass through it based on the geometry, spacing, and material properties of its constituent elements. It is often used as a passive component in microwave and millimeter-wave devices, such as antennas, radars, filters, and absorbers.\n", "\n", "This notebook provides a demonstration of a microwave FSS composed of copper cross structures. The copper is defined as [LossyMetalMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LossyMetalMedium.html), which is modeled as surface impedance boundary condition (SIBC). It works accurately even though the copper layer is very thin compared to the wavelength (~2.5 cm), and hence the grid step size around the copper layer, so it's computational efficiency. The FSS has been designed to exhibit a stop band at 12 GHz, where the transmission (S21) reaches as low as -50 dB. By visualizing the field distribution at the resonant frequency, we can observe the dipolar resonance feature of the copper structure. This simulation showcases the effectiveness of FSSs as passive components in microwave devices, and their ability to manipulate electromagnetic waves with high precision.\n", "\n", "\"Schematic\n", "\n", "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. For more simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it." ] }, { "cell_type": "code", "execution_count": 1, "id": "5cb1ff77", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:21.745229Z", "iopub.status.busy": "2025-12-05T22:47:21.745123Z", "iopub.status.idle": "2025-12-05T22:47:23.769903Z", "shell.execute_reply": "2025-12-05T22:47:23.769411Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.rf as rf\n", "import tidy3d.web as web" ] }, { "cell_type": "markdown", "id": "3f913f7e", "metadata": {}, "source": [ "## Simulation Setup" ] }, { "cell_type": "markdown", "id": "4911bc70", "metadata": {}, "source": [ "The default frequency unit in `Tidy3D` is Hz. For convenience, we prefer to work with GHz in this example. A frequency range from 10 GHz to 14 GHz is studied while the FSS is designed to resonate at 12 GHz." ] }, { "cell_type": "code", "execution_count": 2, "id": "7cdf5e48", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.771837Z", "iopub.status.busy": "2025-12-05T22:47:23.771493Z", "iopub.status.idle": "2025-12-05T22:47:23.773923Z", "shell.execute_reply": "2025-12-05T22:47:23.773574Z" } }, "outputs": [], "source": [ "GHz = 1e9 # 1 GHz = 1e9 Hz\n", "\n", "freq_low, freq_high = 10 * GHz, 14 * GHz\n", "freq0 = 12 * GHz # central frequency\n", "freqs = np.linspace(freq_low, freq_high, 500) # frequency range of interest\n", "\n", "fwidth = 0.5 * (freq_high - freq_low) # width of the source spectrum\n", "\n", "lda0 = td.C_0 / freq0 # central wavelength" ] }, { "cell_type": "markdown", "id": "a4eebf85", "metadata": {}, "source": [ "The default length unit in Tidy3D is $\\mu m$. For convenience, we prefer to work with mm in this example. Here we define the geometric parameters such as the length and width of the cross structure." ] }, { "cell_type": "code", "execution_count": 3, "id": "17b42904", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.775061Z", "iopub.status.busy": "2025-12-05T22:47:23.774966Z", "iopub.status.idle": "2025-12-05T22:47:23.776775Z", "shell.execute_reply": "2025-12-05T22:47:23.776433Z" } }, "outputs": [], "source": [ "mm = 1e3 # 1 mm = 1e3 um\n", "P = 15 * mm # periodicity of the unit cell\n", "L = 9.4 * mm # length of the cross\n", "W = 2 * mm # width of the cross\n", "t_sub = 2.2 * mm # thickness of the substrate\n", "t_copper = 0.1 * mm # thickness of the copper layer" ] }, { "cell_type": "markdown", "id": "2eb43d28", "metadata": {}, "source": [ "The copper is modeled as [LossyMetalMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LossyMetalMedium.html), or SIBC internally. We don't need to use very fine grid to resolve the actual thickness of the copper layer, as long as the copper layer is at the primal grid along the thickness dimension. The conductivity of copper is about $5\\times 10^7$ S/m, which is 50 S/$\\mu m$." ] }, { "cell_type": "code", "execution_count": 4, "id": "fbce46a3", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.777582Z", "iopub.status.busy": "2025-12-05T22:47:23.777490Z", "iopub.status.idle": "2025-12-05T22:47:23.779376Z", "shell.execute_reply": "2025-12-05T22:47:23.779106Z" } }, "outputs": [], "source": [ "sigma_copper = 50 # copper conductivity in S/um\n", "copper = rf.LossyMetalMedium(conductivity=sigma_copper, frequency_range=(freq_low, freq_high))\n", "\n", "eps_sub = 2.5 # permittivity of the substrate\n", "sub_medium = td.Medium(permittivity=eps_sub) # define substrate medium" ] }, { "cell_type": "code", "execution_count": 5, "id": "c49e7a95", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.780538Z", "iopub.status.busy": "2025-12-05T22:47:23.780448Z", "iopub.status.idle": "2025-12-05T22:47:23.782910Z", "shell.execute_reply": "2025-12-05T22:47:23.782634Z" } }, "outputs": [], "source": [ "cross = []\n", "cross.append(\n", " td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-L / 2, -W / 2, t_sub), rmax=(L / 2, W / 2, t_sub + t_copper)\n", " ),\n", " medium=copper,\n", " )\n", ")\n", "cross.append(\n", " td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-W / 2, -L / 2, t_sub), rmax=(W / 2, L / 2, t_sub + t_copper)\n", " ),\n", " medium=copper,\n", " )\n", ")\n", "\n", "substrate = td.Structure(\n", " geometry=td.Box(center=(0, 0, t_sub / 2), size=(td.inf, td.inf, t_sub)),\n", " medium=sub_medium,\n", ")" ] }, { "cell_type": "markdown", "id": "acf9173e", "metadata": {}, "source": [ "A [PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) source polarized in the x direction is added as the incident wave from the top of the FSS. To measure reflection (S11) and transmission (S21), two [FluxMonitors](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html) are added on the top and bottom of the simulation domain. Lastly, to visualize the resonant mode field, a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) is added to the copper layer plane." ] }, { "cell_type": "code", "execution_count": 6, "id": "d0fb135b", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.783920Z", "iopub.status.busy": "2025-12-05T22:47:23.783833Z", "iopub.status.idle": "2025-12-05T22:47:23.787648Z", "shell.execute_reply": "2025-12-05T22:47:23.787356Z" } }, "outputs": [], "source": [ "offset = lda0 / 2 # extra spacing added in the positive and negative z directions\n", "\n", "# define a plane wave source\n", "plane_wave = td.PlaneWave(\n", " center=(0, 0, t_sub + 0.1 * offset),\n", " size=(td.inf, td.inf, 0),\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", " direction=\"-\",\n", ")\n", "\n", "# define a flux monitor to measure reflection\n", "S11_monitor = td.FluxMonitor(\n", " center=(0, 0, t_sub + offset),\n", " size=(td.inf, td.inf, 0),\n", " freqs=freqs,\n", " name=\"S11\",\n", ")\n", "\n", "# define a flux monitor to measure reflection\n", "S21_monitor = td.FluxMonitor(\n", " center=(0, 0, -t_sub - offset),\n", " size=(td.inf, td.inf, 0),\n", " freqs=freqs,\n", " name=\"S21\",\n", " normal_dir=\"-\",\n", ")\n", "\n", "# define a field monitor to visualize field distribution\n", "field_monitor = td.FieldMonitor(\n", " center=(0, 0, t_sub), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n", ")" ] }, { "cell_type": "markdown", "id": "c69509c7", "metadata": {}, "source": [ "With the previously defined structures, source, and monitors, we are ready to define a `Tidy3D` [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html). Periodic boundary condition is applied in the $x$ and $y$ directions while [PML](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PML.html) is applied in the $z$ direction. \n", "\n", "We also set up automatic nonuniform grids. In addition, we use [LayerRefinementSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.LayerRefinementSpec.html) to refine mesh around corners." ] }, { "cell_type": "code", "execution_count": 7, "id": "1a42e3b2", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.788848Z", "iopub.status.busy": "2025-12-05T22:47:23.788750Z", "iopub.status.idle": "2025-12-05T22:47:23.920462Z", "shell.execute_reply": "2025-12-05T22:47:23.920041Z" } }, "outputs": [ { "data": { "text/html": [ "
17:47:23 EST WARNING: A structure has a nonzero dimension along axis z, which is\n",
       "             however too small compared to the generated mesh step along that   \n",
       "             direction. This could produce unpredictable results. We recommend  \n",
       "             increasing the resolution, or adding a mesh override structure to  \n",
       "             ensure that all geometries are at least one pixel thick along all  \n",
       "             dimensions.                                                        \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m17:47:23 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: A structure has a nonzero dimension along axis z, which is\u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mhowever too small compared to the generated mesh step along that \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mdirection. This could produce unpredictable results. We recommend \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mincreasing the resolution, or adding a mesh override structure to \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mensure that all geometries are at least one pixel thick along all \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mdimensions. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             WARNING: Suppressed 1 WARNING message.                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m1\u001b[0m\u001b[31m WARNING message. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             WARNING:  ℹ️ ⚠️ RF simulations are subject to new license            \n",
       "             requirements in the future. You are using RF-specific components in\n",
       "             this simulation.                                                   \n",
       "              - Contains a 'LossyMetalMedium'.                                  \n",
       "              - Contains sources defined for RF wavelengths.                    \n",
       "              - Contains monitors defined for RF wavelengths.                   \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: ℹ️ ⚠️ RF simulations are subject to new license \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mrequirements in the future. You are using RF-specific components in\u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31mthis simulation. \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31m - Contains a \u001b[0m\u001b[32m'LossyMetalMedium'\u001b[0m\u001b[31m. \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31m - Contains sources defined for RF wavelengths. \u001b[0m\n", "\u001b[2;36m \u001b[0m\u001b[31m - Contains monitors defined for RF wavelengths. \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# simulation domain size in z\n", "Lz = t_sub + 2.2 * offset\n", "\n", "# define a BoundarySpec\n", "boundary_spec = td.BoundarySpec(\n", " x=td.Boundary.periodic(),\n", " y=td.Boundary.periodic(),\n", " z=td.Boundary(minus=td.PML(), plus=td.PML()),\n", ")\n", "\n", "# define a GridSpec\n", "layer_spec = rf.LayerRefinementSpec.from_structures(\n", " axis=2,\n", " structures=cross,\n", " corner_refinement=td.GridRefinement(refinement_factor=5),\n", ")\n", "\n", "grid_spec = td.GridSpec.auto(\n", " min_steps_per_wvl=30,\n", " layer_refinement_specs=[layer_spec],\n", " wavelength=lda0,\n", ")\n", "\n", "run_time = 1e-8 # simulation run time\n", "\n", "# define simulation\n", "sim = td.Simulation(\n", " size=(P, P, Lz),\n", " grid_spec=grid_spec,\n", " structures=[substrate] + cross,\n", " sources=[plane_wave],\n", " monitors=[S11_monitor, S21_monitor, field_monitor],\n", " run_time=run_time,\n", " boundary_spec=boundary_spec,\n", " symmetry=(-1, 1, 0), # symmetry is used to reduce the computational load\n", " subpixel=td.SubpixelSpec(\n", " lossy_metal=td.SurfaceImpedance(edge_singularity_correction=True)\n", " ), # edge singularity correction for faster convergence\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "91613032-e560-449a-9d2d-e377a310d9dd", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.921782Z", "iopub.status.busy": "2025-12-05T22:47:23.921660Z", "iopub.status.idle": "2025-12-05T22:47:23.924182Z", "shell.execute_reply": "2025-12-05T22:47:23.923773Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'Nx': 58, 'Ny': 58, 'Nz': 64, 'grid_points': 215296, 'min_grid_size': 124.91352416666632, 'max_grid_size': 832.7568277777791, 'computational_complexity': 1723.5603705547571}\n" ] } ], "source": [ "print(sim.grid_info)" ] }, { "cell_type": "markdown", "id": "93cc2c38", "metadata": {}, "source": [ "Before submitting the simulation job to the server, we can validate the simulation setup by plotting it. Here we overlay the grids on top to make sure the grid is sufficiently fine compared to the structure sizes." ] }, { "cell_type": "code", "execution_count": 9, "id": "21d5ee79", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:23.925175Z", "iopub.status.busy": "2025-12-05T22:47:23.925060Z", "iopub.status.idle": "2025-12-05T22:47:24.235210Z", "shell.execute_reply": "2025-12-05T22:47:24.234871Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(1, 2, tight_layout=True, figsize=(10, 5))\n", "sim.plot(z=t_sub, ax=ax[0])\n", "sim.plot_grid(z=t_sub, ax=ax[0])\n", "ax[0].set_xlim(0, 5000)\n", "ax[0].set_ylim(0, 5000)\n", "\n", "sim.plot(x=0, ax=ax[1])\n", "sim.plot_grid(x=0, ax=ax[1])\n", "ax[1].set_xlim(0, 5000)\n", "ax[1].set_ylim(-2000, 5000)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d7acb394", "metadata": {}, "source": [ "Submit the simulation job to the server." ] }, { "cell_type": "code", "execution_count": 10, "id": "ced2b1d8", "metadata": { "execution": { "iopub.execute_input": "2025-12-05T22:47:24.236744Z", "iopub.status.busy": "2025-12-05T22:47:24.236614Z", "iopub.status.idle": "2025-12-05T22:48:07.895982Z", "shell.execute_reply": "2025-12-05T22:48:07.895389Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
17:47:24 EST Created task 'frequency_selective_surface' with resource_id        \n",
       "             'fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd' and task_type 'FDTD'.  \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m17:47:24 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'frequency_selective_surface'\u001b[0m with resource_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29c\n",
       "             e-4dc0-a51e-2a6cb9fbffbd'.                                         \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", "\u001b[2;36m \u001b[0m\u001b]8;id=985304;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=122252;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=985304;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=237109;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=985304;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[32m-c804006f-29c\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=985304;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[32me-4dc0-a51e-2a6cb9fbffbd'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             Task folder: 'default'.                                            \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=341458;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "89bb0f2724144368b721b32f73e7e062", "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": [
       "
17:47:34 EST 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",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:47:34 EST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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": [
       "
17:47:35 EST status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:47:35 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             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",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8f741735ede443a8d789f185f917aca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:47:58 EST status = preprocess                                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:47:58 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:48:01 EST status = postprocess                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:48:01 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:48:03 EST status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:48:03 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:48:05 EST starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:48:05 EST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a00c8fb58574d6e8cbb1ca9e59e3a19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:48:06 EST early shutoff detected at 16%, exiting.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:48:06 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             View simulation result at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29c\n",
       "             e-4dc0-a51e-2a6cb9fbffbd'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=491229;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=108723;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=491229;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=272558;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=491229;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[4;34m-c804006f-29c\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=491229;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c804006f-29ce-4dc0-a51e-2a6cb9fbffbd\u001b\\\u001b[4;34me-4dc0-a51e-2a6cb9fbffbd'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cea05e88639d4457b39431f114d414fa",
       "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": [
       "
17:48:07 EST Loading simulation from data/simulation_data.hdf5                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:48:07 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from data/simulation_data.hdf5                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: A structure has a nonzero dimension along axis z, which is\n",
       "             however too small compared to the generated mesh step along that   \n",
       "             direction. This could produce unpredictable results. We recommend  \n",
       "             increasing the resolution, or adding a mesh override structure to  \n",
       "             ensure that all geometries are at least one pixel thick along all  \n",
       "             dimensions.                                                        \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: A structure has a nonzero dimension along axis z, which is\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mhowever too small compared to the generated mesh step along that   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mdirection. This could produce unpredictable results. We recommend  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mincreasing the resolution, or adding a mesh override structure to  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mensure that all geometries are at least one pixel thick along all  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mdimensions.                                                        \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Suppressed 1 WARNING message.                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Suppressed \u001b[0m\u001b[1;36m1\u001b[0m\u001b[31m WARNING message.                             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Warning messages were found in the solver log. For more   \n",
       "             information, check 'SimulationData.log' or use                     \n",
       "             'web.download_log(task_id)'.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job = web.Job(simulation=sim, task_name=\"frequency_selective_surface\")\n",
    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398bb3f5",
   "metadata": {},
   "source": [
    "## Result Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c77716d",
   "metadata": {},
   "source": [
    "After the simulation is complete, we first visualize the S-parameters. A prominent resonance feature is observed at 12 GHz where S21 reaches -50 dB. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "765dc01b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-05T22:48:08.140776Z",
     "iopub.status.busy": "2025-12-05T22:48:08.140701Z",
     "iopub.status.idle": "2025-12-05T22:48:08.211780Z",
     "shell.execute_reply": "2025-12-05T22:48:08.211376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S11 = sim_data[\"S11\"].flux\n",
    "S21 = sim_data[\"S21\"].flux\n",
    "\n",
    "plt.plot(freqs / GHz, 10 * np.log10(S11), label=\"S11\")\n",
    "plt.plot(freqs / GHz, 10 * np.log10(S21), label=\"S21\")\n",
    "plt.xlim(10, 14)\n",
    "plt.ylim(-60, 0)\n",
    "plt.xlabel(\"Frequency (GHz)\")\n",
    "plt.ylabel(\"S-parameters (dB)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75414fcf",
   "metadata": {},
   "source": [
    "Lastly, we plot the field intensity distribution at the resonant frequency. A strong dipolar field pattern is observed in the horizontal copper pattern. This dipolar resonance is responsible for the total reflection of the electromagnetic wave at this frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c9bb0ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-05T22:48:08.213115Z",
     "iopub.status.busy": "2025-12-05T22:48:08.212962Z",
     "iopub.status.idle": "2025-12-05T22:48:08.392866Z",
     "shell.execute_reply": "2025-12-05T22:48:08.392443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d88f2e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures",
   "Microwave and RF devices"
  ],
  "description": "This notebook demonstrates how to model a microwave frequency selective surface in Tidy3D FDTD.",
  "feature_image": "./img/frequency_selective_surface.png",
  "features": [
   "2D material"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "frequency selective surface, FSS, microwave, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  },
  "title": "Microwave Frequency Selective Surface | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "1998640d1e5e430da3978f0f3c913d34": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "5941d1f8625443868366b7fb48bbf712": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "678ff77e33004e2184a0709903d36d34": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "6a00c8fb58574d6e8cbb1ca9e59e3a19": {
      "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_5941d1f8625443868366b7fb48bbf712",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
solver progress (field decay = 5.17e-06) ━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\n
\n",
          "text/plain": "solver progress (field decay = 5.17e-06) \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "89bb0f2724144368b721b32f73e7e062": {
      "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_1998640d1e5e430da3978f0f3c913d34",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
↑ simulation.hdf5.gz ━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 5.8/5.8 kB • ? • 0:00:00\n
\n",
          "text/plain": "\u001b[1;31m↑\u001b[0m \u001b[1;34msimulation.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m5.8/5.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "b4e496b4ac99440c8c64833815cf27c3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "cea05e88639d4457b39431f114d414fa": {
      "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_b4e496b4ac99440c8c64833815cf27c3",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
↓ simulation_data.hdf5.gz ━━━━━━━━━━━━━━━━━━ 100.0% • 56.8/56.8 kB • ? • 0:00:00\n
\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34msimulation_data.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m56.8/56.8 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "d8f741735ede443a8d789f185f917aca": {
      "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_678ff77e33004e2184a0709903d36d34",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "
🚶  Waiting for 'frequency_selective_surface'...\n
\n",
          "text/plain": "\u001b[32m🚶 \u001b[0m \u001b[1;32mWaiting for 'frequency_selective_surface'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}