{ "cells": [ { "cell_type": "markdown", "id": "ad83ae02", "metadata": {}, "source": [ "# High Q factor plasmonic thermal emitter with surface lattice resonance\n", "\n", "**Author: Guodong Zhu, Vanderbilt University**\n", "\n", "In this notebook, we simulate a high-Q-factor plasmonic thermal emitter utilizing surface lattice resonance. By adjusting the design of periodic nanostructures, sharp resonance modes can be supported, resulting in enhanced and spectrally selective thermal emission.\n", "\n", "The model consists of a unit cell with periodic boundary conditions. Absorption is calculated under plane wave illumination. According to Kirchhoff’s law, emission equals absorption at thermal equilibrium, allowing us to use the absorption results to determine the thermal emission properties of the structure.\n", "\n", "\"Schematic\"\n", "\n", "This notebook is associated with the publication `G. Zhu, I. Hong, K. Li, et al. “ Engineering Thermal Emission with Enhanced Emissivity and Quality Factor Using Bound States in the Continuum and Electromagnetically Induced Absorption.” Adv. Optical Mater. (2025).` [DOI: 10.1002/adom.202501257](https://doi.org/10.1002/adom.202501257).\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:32:56.659967Z", "start_time": "2025-08-02T17:32:56.656194Z" }, "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "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": "a82f5b76", "metadata": {}, "source": [ "## Simulation Setup\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "c3faf33ae2a8b95a", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:32:56.876498Z", "start_time": "2025-08-02T17:32:56.874459Z" } }, "outputs": [], "source": [ "# units definitions\n", "um = 1e0\n", "nm = 1e-3\n", "wavelength = 4.0 # center wavelength" ] }, { "cell_type": "markdown", "id": "3896f727", "metadata": {}, "source": [ "Since the simulations might involve resonances, a relatively long runtime is set." ] }, { "cell_type": "code", "execution_count": 3, "id": "29dfefbd1acfd972", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:32:57.097013Z", "start_time": "2025-08-02T17:32:57.094005Z" } }, "outputs": [], "source": [ "wavelengths = np.linspace(wavelength - 1 * um, wavelength + 1 * um, 1001)\n", "freqs = td.C_0 / wavelengths\n", "freq0 = (np.max(freqs) + np.min(freqs)) / 2.0 # center frequency\n", "fwidth = np.max(freqs) - np.min(freqs)\n", "run_time = 5e-11" ] }, { "cell_type": "code", "execution_count": 4, "id": "dad2d5a7282d8c45", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:32:57.302700Z", "start_time": "2025-08-02T17:32:57.299355Z" } }, "outputs": [], "source": [ "# defining mediums\n", "medium_Al2O3 = td.Medium(permittivity=1.685**2)\n", "medium_gold = td.material_library[\"Au\"][\"Olmon2012evaporated\"]\n", "medium_air = td.Medium(permittivity=1**2)" ] }, { "cell_type": "code", "execution_count": 5, "id": "9bf6a5518c032188", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:32:57.528352Z", "start_time": "2025-08-02T17:32:57.524741Z" } }, "outputs": [], "source": [ "size_design_x = 4000 * nm\n", "size_design_y = 4000 * nm\n", "thick_metal = 100 * nm\n", "thick_spacer = 0.15\n", "thick_reflector = 0.15\n", "thick_sub = 8.0 - thick_spacer - thick_reflector\n", "thick_air = 8.0 - thick_metal\n", "\n", "mask_radius = 1690 * nm\n", "size_x = size_design_x\n", "size_y = size_design_y\n", "size_z = thick_sub + thick_metal + thick_air + thick_spacer + thick_reflector\n", "top_z = size_z / 2.0\n", "\n", "center_metal_z = top_z - thick_air - thick_metal / 2.0\n", "center_spacer_z = top_z - thick_air - thick_metal - thick_spacer / 2.0\n", "center_reflector_z = (\n", " top_z - thick_air - thick_metal - thick_spacer - thick_reflector / 2.0\n", ")" ] }, { "cell_type": "markdown", "id": "ebe05e5b", "metadata": {}, "source": [ "Auxiliary function to define the geometry.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "4bb282dab42c8a27", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:32:57.762862Z", "start_time": "2025-08-02T17:32:57.758706Z" } }, "outputs": [], "source": [ "def sector_vertices(x0, y0, R, n, theta):\n", " vertices = []\n", " vertices.append((x0, y0))\n", "\n", " for i in range(n, 0, -1):\n", " theta_i = i * theta / n\n", " Xp = x0 + R * np.cos(theta_i)\n", " Yp = y0 + R * np.sin(theta_i)\n", " vertices.append((Xp, Yp))\n", " for i in range(0, n + 1, 1):\n", " theta_i = -i * theta / n\n", " Xp = x0 + R * np.cos(theta_i)\n", " Yp = y0 + R * np.sin(theta_i)\n", " vertices.append((Xp, Yp))\n", "\n", " return vertices" ] }, { "cell_type": "markdown", "id": "12b7b11f", "metadata": {}, "source": [ "Next, we create an auxiliary function to return the [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) object as a function of the incident angle. Note that we will use a `FixedAngleSpec` for the [PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) `angular_spec` argument, which ensures that the same angle is fixed for the entire source spectrum. A more detailed discussion can be found [here](https://docs.simulation.cloud/projects/tidy3d/en/latest/notebooks/BroadbandPlaneWaveWithConstantObliqueIncidentAngle.html).\n", "\n", "Due to geometric features that are much smaller than the wavelength, we will also add a [MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MeshOverrideStructure.html) region to locally refine the mesh and correctly resolve the geometry.\n", "\n", "Due to resonant effects and to reduce computational cost, we will reduce the default simulation [shutoff](https://home.dev-simulation.cloud/tidy3d/learning-center/tidy3d-gui/Lecture-8-Run-Time-and-Shutoff/) to $10^{-4}$." ] }, { "cell_type": "code", "execution_count": 7, "id": "d2673a7e695dbab3", "metadata": { "ExecuteTime": { "end_time": "2025-08-03T19:22:11.285731Z", "start_time": "2025-08-03T19:22:11.278267Z" } }, "outputs": [], "source": [ "def make_sim(\n", " polarization_angle: float,\n", " incident_angle: float = 0,\n", " outerR: float = 1.68,\n", " innerR: float = 1.04,\n", ") -> td.Simulation:\n", " # structures\n", " sector = td.Structure(\n", " geometry=td.PolySlab(\n", " vertices=sector_vertices(0, 0, outerR, 20, np.deg2rad(30)),\n", " axis=2,\n", " slab_bounds=(0, thick_metal),\n", " ),\n", " medium=medium_gold,\n", " )\n", " cylinder_air_2 = td.Structure(\n", " geometry=td.Cylinder(\n", " axis=2,\n", " radius=1.29,\n", " center=(0, 0, thick_metal / 2.0),\n", " length=thick_metal,\n", " ),\n", " medium=medium_air,\n", " )\n", " cylinder = td.Structure(\n", " geometry=td.Cylinder(\n", " axis=2,\n", " radius=innerR,\n", " center=(0, 0, thick_metal / 2.0),\n", " length=thick_metal,\n", " ),\n", " medium=medium_gold,\n", " )\n", "\n", " cylinder_air = td.Structure(\n", " geometry=td.Cylinder(\n", " axis=2,\n", " radius=innerR / 2.0,\n", " center=(0, 0, thick_metal / 2.0),\n", " length=thick_metal,\n", " ),\n", " medium=medium_air,\n", " )\n", "\n", " spacer = td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-td.inf, -td.inf, center_spacer_z - thick_spacer / 2.0),\n", " rmax=(+td.inf, +td.inf, center_spacer_z + thick_spacer / 2.0),\n", " ),\n", " medium=medium_Al2O3,\n", " )\n", "\n", " reflector = td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-td.inf, -td.inf, center_reflector_z - thick_reflector / 2.0),\n", " rmax=(+td.inf, +td.inf, center_reflector_z + thick_reflector / 2.0),\n", " ),\n", " medium=medium_gold,\n", " )\n", "\n", " substrate = td.Structure(\n", " geometry=td.Box(\n", " center=(0, 0, -size_z + thick_sub),\n", " size=(td.inf, td.inf, size_z),\n", " ),\n", " medium=medium_Al2O3,\n", " )\n", "\n", " # plane wave source\n", " plane_wave = td.PlaneWave(\n", " center=(0, 0, size_z / 2 - 2),\n", " size=(td.inf, td.inf, 0),\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", " pol_angle=polarization_angle,\n", " angle_theta=incident_angle,\n", " direction=\"-\",\n", " angular_spec=td.FixedAngleSpec(),\n", " )\n", "\n", " # monitors\n", " reflection = td.FluxMonitor(\n", " center=(0, 0, size_z / 2 - 1),\n", " size=(td.inf, td.inf, 0),\n", " freqs=freqs,\n", " name=\"reflection\",\n", " )\n", "\n", " transmission = td.FluxMonitor(\n", " center=(0, 0, -size_z / 2 + 1),\n", " size=(td.inf, td.inf, 0),\n", " freqs=freqs,\n", " name=\"transmission\",\n", " )\n", "\n", " # mesh refinement\n", " mesh_overrides = [\n", " td.MeshOverrideStructure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-td.inf, -td.inf, center_reflector_z - thick_reflector / 2.0),\n", " rmax=(+td.inf, +td.inf, center_metal_z + thick_metal / 2.0),\n", " ),\n", " dl=[80 * nm, 80 * nm, 20 * nm],\n", " )\n", " ]\n", "\n", " my_sim = td.Simulation(\n", " size=(size_x, size_y, size_z),\n", " run_time=run_time,\n", " structures=[\n", " substrate,\n", " spacer,\n", " reflector,\n", " sector,\n", " cylinder_air_2,\n", " cylinder,\n", " cylinder_air,\n", " ],\n", " sources=[plane_wave],\n", " monitors=[reflection, transmission],\n", " boundary_spec=td.BoundarySpec(\n", " x=td.Boundary.periodic(),\n", " y=td.Boundary.periodic(),\n", " z=td.Boundary(plus=td.PML(), minus=td.Absorber(num_layers=80)),\n", " ),\n", " grid_spec=td.GridSpec.auto(\n", " wavelength=wavelength,\n", " min_steps_per_wvl=20,\n", " override_structures=mesh_overrides,\n", " ),\n", " medium=medium_air,\n", " shutoff=1e-4,\n", " )\n", " return my_sim" ] }, { "cell_type": "code", "execution_count": 8, "id": "34d3055f80ce8de4", "metadata": { "ExecuteTime": { "end_time": "2025-08-03T19:22:14.301247Z", "start_time": "2025-08-03T19:22:14.285599Z" } }, "outputs": [], "source": [ "sim = make_sim(np.deg2rad(0.0), np.deg2rad(0.0), 1.68, 1.04)" ] }, { "cell_type": "markdown", "id": "1e5c90a7", "metadata": {}, "source": [ "Next, we plot the grid to ensure that the structure is properly resolved by the mesh.\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "44fce720385798a0", "metadata": { "ExecuteTime": { "end_time": "2025-08-03T19:22:16.961079Z", "start_time": "2025-08-03T19:22:16.866733Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sim.plot(z=center_metal_z)\n", "sim.plot_grid(z=center_metal_z, ax=ax)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "319fe477", "metadata": {}, "source": [ "## Postprocessing\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "4e07f752bdf96c30", "metadata": { "ExecuteTime": { "end_time": "2025-08-02T17:33:48.135284Z", "start_time": "2025-08-02T17:33:07.503222Z" } }, "outputs": [ { "data": { "text/html": [ "
17:19:43 -03 Created task 'simulation_at_normal_incident' with task_id          \n",
       "             'fdve-ff872fab-3c73-424d-a648-46f2149ba891' and task_type 'FDTD'.  \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m17:19:43 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'simulation_at_normal_incident'\u001b[0m with task_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-ff872fab-3c73-424d-a648-46f2149ba891'\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-ff872fab-3c7\n",
       "             3-424d-a648-46f2149ba891'.                                         \n",
       "
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             Task folder: 'default'.                                            \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=735348;https://tidy3d.simulation.cloud/folders/folder-dc5128c7-562b-4bcf-a539-9356f1d308ca\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "508d4d8ead60456984527880cf1c9582", "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": [
       "
17:19:46 -03 Maximum FlexCredit cost: 0.212. 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;36m17:19:46 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.212\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": [
       "
17:19:47 -03 status = success                                                   \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m17:19:47 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      "text/plain": [
       "Output()"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:19:49 -03 loading simulation from simulation_data.hdf5                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:19:49 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = td.web.run(sim, task_name=\"simulation_at_normal_incident\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f75f60",
   "metadata": {},
   "source": [
    "We can calculate the absorption using the relation $1 = A + R + T$, where $A$ is the absorption, $R$ is the reflection, and $T$ is the transmission.\n",
    "\n",
    "A strong absorption peak can be seen around 4.1 µm.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e69b1309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = sim_data[\"reflection\"].flux\n",
    "T = abs(sim_data[\"transmission\"].flux)\n",
    "plt.plot(wavelengths, 1 - (R + T))\n",
    "plt.xlabel(\"Wavelength(µm)\")\n",
    "plt.ylabel(\"Absorption(a.u.)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78c284875ba3ad9",
   "metadata": {},
   "source": [
    "## Band Structure\n",
    "\n",
    "Next, we run a [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) of simulations, varying the incident angle to analyze the angular emission.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cee26a41f2c19dc6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-03T16:24:30.640316Z",
     "start_time": "2025-08-03T16:23:16.375068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
17:20:30 -03 Maximum FlexCredit cost: 6.943 for the whole batch.                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:20:30 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.943\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "6.9431660107445525"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "angle_list = np.linspace(0, 6, 11)  # theta values for the parameter sweep\n",
    "# create a dictionary of simulations\n",
    "sims = {\n",
    "    f\"theta={theta:.1f}\": make_sim(np.deg2rad(0), np.deg2rad(theta), 1.68, 1.04)\n",
    "    for theta in angle_list\n",
    "}\n",
    "\n",
    "# create and run a batch of simulations\n",
    "batch = web.Batch(simulations=sims, folder_name=\"sweep_angle_ring\")\n",
    "batch.estimate_cost()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2dcba3675222130",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-14T20:18:41.310922Z",
     "start_time": "2025-06-14T20:12:26.579294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7669875e323a4018b5975933acc600f6",
       "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": [
       "
             Started working on Batch containing 11 tasks.                      \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m11\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:20:43 -03 Maximum FlexCredit cost: 6.943 for the whole batch.                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:20:43 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m6.943\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Use 'Batch.real_cost()' to get the billed FlexCredit cost after the\n",
       "             Batch has completed.                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5830f66d756e4096856f0b4e292149d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
17:20:52 -03 Batch complete.                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:20:52 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f3428a9e95140aa9b0aa2f37f39a601",
       "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"
    }
   ],
   "source": [
    "batch_results = batch.run(path_dir=\"sweep_angle_ring\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc1d568",
   "metadata": {},
   "source": [
    "After running, we can check the real billed cost for the whole batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85961c96c9a44dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-14T20:18:43.343782Z",
     "start_time": "2025-06-14T20:18:41.318688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
17:21:06 -03 Total billed flex credit cost: 8.912.                              \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m17:21:06 -03\u001b[0m\u001b[2;36m \u001b[0mTotal billed flex credit cost: \u001b[1;36m8.912\u001b[0m.                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "8.91216872032322"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.real_cost()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c68609296e946ca4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-02T17:31:31.038981Z",
     "start_time": "2025-08-02T17:31:21.881839Z"
    }
   },
   "outputs": [],
   "source": [
    "td.config.logging_level = \"ERROR\"\n",
    "\n",
    "R = np.array(\n",
    "    [\n",
    "        np.abs(batch_results[f\"theta={theta:.1f}\"][\"reflection\"].flux.values)\n",
    "        for theta in angle_list\n",
    "    ]\n",
    ")\n",
    "\n",
    "T = np.array(\n",
    "    [\n",
    "        np.abs(batch_results[f\"theta={theta:.1f}\"][\"transmission\"].flux.values)\n",
    "        for theta in angle_list\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd00370c",
   "metadata": {},
   "source": [
    "Finally, we can visualize the absorption as a function of the incident angle and compare it with the diffraction results. This allows us to see how the structure’s resonances relate to the underlying lattice diffraction conditions and to distinguish between purely geometric diffraction features and true surface lattice resonances.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c6ebe36b07ae617a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-02T17:32:06.225403Z",
     "start_time": "2025-08-02T17:32:06.067818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Absorption_matrix = 1.0 - (T + R)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(5.5, 8))\n",
    "absorption_all = np.concatenate((Absorption_matrix[::-1, :], Absorption_matrix), axis=0)\n",
    "theta_list_all = np.concatenate((-angle_list[::-1], angle_list), axis=0)\n",
    "ks = np.sin(np.deg2rad(theta_list_all))\n",
    "im = ax1.pcolormesh(\n",
    "    theta_list_all, td.C_0 / freqs, absorption_all.T, cmap=\"hot\", vmin=0, vmax=1\n",
    ")\n",
    "ax1.set_xlabel(\"Incident angle (degrees)\", fontsize=12)\n",
    "ax1.set_xlim([-6, 6])\n",
    "ax1.set_ylim([3, 5])\n",
    "ax1.set_ylabel(r\"wavelength ($\\mu$m)\", fontsize=12)\n",
    "ax1.tick_params(width=1, labelsize=12)\n",
    "cbar = plt.colorbar(im, pad=0.05, orientation=\"horizontal\", location=\"top\")\n",
    "cbar.set_ticks([0.0, 1.0])\n",
    "cbar.ax.tick_params(labelsize=12)\n",
    "cbar.set_label(\"Absorptance\", fontsize=12)\n",
    "cbar.ax.tick_params(labelsize=12)\n",
    "\n",
    "# Constants\n",
    "c = td.C_0  # speed of light [um/s]\n",
    "Px = 4.0  # lattice period in x [um]\n",
    "Py = 4.0  # lattice period in y [um]\n",
    "\n",
    "# Diffraction orders\n",
    "orders = np.array(\n",
    "    [\n",
    "        [1, 0],\n",
    "        [-1, 0],\n",
    "        [0, 1],\n",
    "        [0, -1],\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Normalized kx values\n",
    "kx_norm = np.linspace(0, 0.5, 500)\n",
    "theta_k = np.rad2deg(np.arcsin(kx_norm))\n",
    "for p, q in orders:\n",
    "    f = np.sqrt(\n",
    "        (kx_norm * 2 * np.pi / Px + p * 2 * np.pi / Px) ** 2 + (q * 2 * np.pi / Py) ** 2\n",
    "    )\n",
    "    kx = 2 * np.pi * kx_norm / Px\n",
    "    wavelengths = 2 * np.pi / f\n",
    "\n",
    "    ax1.plot(theta_k, wavelengths, \"--\", label=f\"({p},{q})\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Plasmonics"
  ],
  "description": "This notebook demonstrates how to simulate a high-Q-factor plasmonic thermal emitter with surface lattice resonance using Tidy3D.",
  "feature_image": "./img/PlasmonicSLR.png",
  "features": [
   "Parameter Sweep"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "plasmonic thermal emitter, surface lattice resonance, periodic nanostructures, 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": "3.11.0"
  },
  "title": "How to model a ring SLR thermal emitter using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}