{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Suppressing artificial reflections with absorber and PML boundaries\n",
        "\n",
        "In an FDTD simulation, Maxwell’s equations are discretized on a finite\n",
        "grid. Simply truncating the computational domain at a boundary can cause\n",
        "outgoing electromagnetic waves to reflect back, contaminating the\n",
        "solution. To prevent these artificial reflections, absorbing boundary\n",
        "conditions are introduced. These boundaries are artificial regions\n",
        "placed at the edge of the computational domain to simulate “open” or\n",
        "unbounded space. Their purpose is to absorb outgoing waves as if the\n",
        "domain extended to infinity, while minimizing reflections back into the\n",
        "interior. Tidy3D offers two primary types of absorbing boundaries:\n",
        "**perfectly matched layers (PML)** and **adiabatic absorbers**.\n",
        "\n",
        "PML uses a mathematically derived “coordinate stretching” or anisotropic\n",
        "material approach to achieve a perfect impedance match at the boundary.\n",
        "By design, PML ideally causes zero reflection at the interface and then\n",
        "exponentially attenuates the wave within the absorbing layer. In\n",
        "contrast, an adiabatic absorber employs a smooth spatial transition in\n",
        "material parameters (e.g., conductivity, permittivity) from the physical\n",
        "domain into an absorbing medium. It aims to minimize, rather than\n",
        "strictly eliminate, reflections by gradually grading the absorption\n",
        "profile so the wave encounters no abrupt transitions.\n",
        "\n",
        "Although PML is more effective at eliminating artificial reflections, it\n",
        "can lead to simulation stability issues, particularly when the structure\n",
        "in the PML is not translationally invariant, the medium is dispersive,\n",
        "or when structures are too close to PML such that evanescent fields leak\n",
        "into it. The adiabatic absorber, on the other hand, is very similar to a\n",
        "physical absorbing region that is purely passive and always stable.\n",
        "Therefore, the choice of absorbing boundary condition depends on the\n",
        "specific simulation setup. If a simulation is diverging with PML,\n",
        "switching to the adiabatic absorber can often resolve the issue. This\n",
        "[waveguide to ring coupling\n",
        "example](https://www.flexcompute.com/tidy3d/examples/notebooks/WaveguideToRingCoupling/)\n",
        "is a canonical example.\n",
        "\n",
        "Many users are concerned that the higher artificial reflections from the\n",
        "adiabatic absorber can result in inaccurate simulation results. However,\n",
        "using a larger number of layers to create a more gradual ramp-up of\n",
        "absorption can effectively mitigate this, albeit at a higher\n",
        "computational cost due to the increased number of FDTD cells required.\n",
        "Thus, achieving the right balance between accuracy and computational\n",
        "cost is crucial. This notebook aims to benchmark the undesired\n",
        "reflections from absorbing boundaries and guide users in selecting the\n",
        "most appropriate settings for their simulations and objectives."
      ],
      "id": "d1e1990f-1787-4377-8e9c-82f2021d0252"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web"
      ],
      "id": "26b381b0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "In this benchmark, we will focus on the telecommunications wavelength\n",
        "and examine the reflection of a basic waveguide mode at the boundary."
      ],
      "id": "a7e25dc7-d41b-43e4-ba20-0b8cdcfbe3c5"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "lda0 = 1.55  # Central wavelength\n",
        "freq0 = td.C_0 / lda0  # Calculate the frequency corresponding to the central wavelength\n",
        "\n",
        "# Create an array of 21 evenly spaced wavelengths between 1.5 and 1.6\n",
        "ldas = np.linspace(1.5, 1.6, 21)\n",
        "\n",
        "# Calculate the frequencies corresponding to each wavelength in the ldas array\n",
        "freqs = td.C_0 / ldas\n",
        "\n",
        "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # Calculate the source frequency width"
      ],
      "id": "e09e2100"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We use a standard single-mode silicon waveguide with a width of 500 nm\n",
        "and a thickness of 220 nm."
      ],
      "id": "a6d576dc-de57-425a-8a52-b26732e936c9"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "w_wg = 0.5  # Width of the waveguide\n",
        "t_wg = 0.22  # Thickness of the waveguide"
      ],
      "id": "3a04a797"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define silicon and oxide as simple nondispersive media."
      ],
      "id": "e6e1e67f-8f41-45d1-9482-f0c4f12657c1"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "n_oxide = 1.44  # Refractive index of oxide\n",
        "\n",
        "oxide = td.Medium(permittivity=n_oxide**2)\n",
        "\n",
        "n_si = 3.47  # Refractive index of silicon\n",
        "si = td.Medium(permittivity=n_si**2)"
      ],
      "id": "3d3f53f5"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create a basic waveguide structure."
      ],
      "id": "6152e05e-2ffb-46d8-8f3a-1295b870a51f"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "waveguide = td.Structure(geometry=td.Box(size=[td.inf, w_wg, t_wg]), medium=si)"
      ],
      "id": "9f25dfb6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we define the size of the simulation domain. For this benchmark,\n",
        "the domain can be relatively small.\n",
        "\n",
        "Afterward, we create a\n",
        "[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
        "to excite the fundamental mode in the waveguide. To measure the\n",
        "reflection from the boundary, we set up a\n",
        "[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)."
      ],
      "id": "bb41512e-5fe4-4457-96fb-db0add36e6b2"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "l_x = 3 * lda0  # Calculate the simulation domain size in the x-direction\n",
        "l_y = w_wg + 2 * lda0  # Calculate the simulation domain size in the y-direction\n",
        "l_z = t_wg + 2 * lda0  # Calculate the simulation domain size in the z-direction\n",
        "\n",
        "buffer = lda0 / 2  # Define a buffer spacing\n",
        "\n",
        "# Create a mode source for excitation\n",
        "modesource = td.ModeSource(\n",
        "    center=[-l_x / 2 + buffer, 0, 0],\n",
        "    size=[0, 3 * w_wg, 5 * t_wg],\n",
        "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
        "    direction=\"+\",\n",
        "    mode_spec=td.ModeSpec(num_modes=1, target_neff=n_si),\n",
        ")\n",
        "\n",
        "# Create a mode monitor to measure reflection at the boundary\n",
        "modemonitor = td.ModeMonitor(\n",
        "    name=\"modemonitor\",\n",
        "    center=[l_x / 2 - buffer, 0, 0],\n",
        "    size=modesource.size,\n",
        "    freqs=freqs,\n",
        "    mode_spec=modesource.mode_spec,\n",
        ")"
      ],
      "id": "d291388c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Adiabatic Absorber\n",
        "\n",
        "We start by defining a function called `make_sim`, which is intended to\n",
        "create a simulation based on three parameters: `num_layers`,\n",
        "`use_absorber`, and `grid_resolution`. The `num_layers` parameter\n",
        "specifies the number of absorbing boundary layers in the simulation,\n",
        "while the `use_absorber` parameter is a boolean that determines whether\n",
        "to use adiabatic absorbers or PML. `grid_resolution` specifies the\n",
        "number of grid points per material wavelength.\n",
        "\n",
        "This function will facilitate a parameter sweep to quantitatively\n",
        "analyze reflection as it relates to the number of boundary layers and\n",
        "grid resolution. In general, using a larger number of layers allows for\n",
        "more gradual absorption and less reflection. For an adiabatic absorber,\n",
        "the reflection also depends on the grid resolution since it affects the\n",
        "layer thickness. For simulations with a finer grid resolution, usually a\n",
        "larger number of absorbers is needed."
      ],
      "id": "38985478-4adb-4153-a037-18613d910981"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_sim(num_layers, use_absorber=True, grid_resolution=25):\n",
        "    # Create a simulation object using the tidy3d library\n",
        "    # Parameters:\n",
        "    # num_layers: Number of layers for the absorbing boundary\n",
        "    # use_absorber: Boolean to determine if absorbing or PML boundaries are used in the x direction\n",
        "    # grid_resolution: Minimum number of grid points per material wavelength\n",
        "\n",
        "    sim = td.Simulation(\n",
        "        size=[l_x, l_y, l_z],\n",
        "        symmetry=[0, -1, 1],\n",
        "        # Define boundary specifications based on whether absorption is enabled\n",
        "        boundary_spec=(\n",
        "            td.BoundarySpec(\n",
        "                x=td.Boundary.absorber(\n",
        "                    num_layers\n",
        "                ),  # Use adiabatic absorber boundary with the specific number of layers in the x direction if use_absorber is True\n",
        "                y=td.Boundary.pml(),\n",
        "                z=td.Boundary.pml(),\n",
        "            )\n",
        "            if use_absorber\n",
        "            else td.BoundarySpec(\n",
        "                x=td.Boundary.pml(\n",
        "                    num_layers\n",
        "                ),  # Use PML with the specified number of layers in the x direction if use_absorber is False\n",
        "                y=td.Boundary.pml(),\n",
        "                z=td.Boundary.pml(),\n",
        "            )\n",
        "        ),\n",
        "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=grid_resolution, wavelength=lda0),\n",
        "        run_time=1e-12,\n",
        "        medium=oxide,\n",
        "        sources=[modesource],\n",
        "        monitors=[modemonitor],\n",
        "        structures=[waveguide],\n",
        "    )\n",
        "\n",
        "    return sim"
      ],
      "id": "92806439"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create a sample simulation and visualize the setup."
      ],
      "id": "597dbd93-e659-42d5-be8f-c4eb3e63a010"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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PfvKTP451J2Eb0pJ/aGhI1WpVnueNuQTKTNH/xZ+/VqvJ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7TCl0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\nvdYa6JIY7EKLalaHb/aJztes/M2+0PmadcEnP/nJT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          }
        }
      ],
      "source": [
        "# Create a simulation object with 40 layers of absorber\n",
        "sim = make_sim(num_layers=40)\n",
        "\n",
        "# Plot the simulation results at z=0 to visualize the simulation\n",
        "sim.plot(z=0)\n",
        "plt.show()"
      ],
      "id": "ca295738"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Reflection as a Function of Number of Layers\n",
        "\n",
        "By default, the adiabatic absorber contains 40 layers. Here, we keep the\n",
        "grid resolution the same and generate a list of absorber layer counts\n",
        "ranging from 10 to 100, with increments of 10. This list will enable us\n",
        "to explore how varying numbers of absorber layers influence reflection.\n",
        "Simultaneously, we will monitor the number of FDTD cells for each\n",
        "simulation, as this could directly affect both the FlexCredit cost and\n",
        "the simulation time.\n",
        "\n",
        "To conduct the parameter sweep, we will create a dictionary of\n",
        "simulations and then use a\n",
        "[Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html)\n",
        "to execute them."
      ],
      "id": "3e1a3dd7-b59c-45f3-bc5a-87d2fdf8fa13"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"759df9d99ca545f4bbe980cf1e532067\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:18 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> tasks.                      \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:28 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.251</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"c7fe789f707443488ca76c697d40484b\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:33 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "num_layers_list = list(\n",
        "    range(10, 101, 10)\n",
        ")  # Create a list of absorber layer counts from 10 to 100 in increments of 10\n",
        "num_cells_list = []  # Initialize an empty list to store the number of cells for each simulation\n",
        "\n",
        "# Initialize an empty dictionary to store simulation objects with their respective layer counts as keys\n",
        "sims = {}\n",
        "\n",
        "# Loop over each number of absorber layers to create simulations\n",
        "for num_layers in num_layers_list:\n",
        "    sim = make_sim(num_layers)\n",
        "    # Append the number of cells (converted to millions) to the num_cells_list\n",
        "    num_cells_list.append(sim.num_cells / 1e6)\n",
        "    sims[f\"{num_layers} layers\"] = sim\n",
        "\n",
        "# Create a batch of simulations\n",
        "batch = web.Batch(simulations=sims, verbose=True)\n",
        "\n",
        "# Run the batch of simulations and save results to the specified directory\n",
        "batch_results = batch.run(path_dir=\"data\")"
      ],
      "id": "84734c21"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After completing the simulations, we can extract and plot the reflection\n",
        "spectrum. The results clearly show that increasing the number of\n",
        "adiabatic absorber layers reduces the reflection. Notably, the default\n",
        "setting of **40 layers** results in a reflection of **-25 dB**, while\n",
        "increasing to **80 layers** reduces the reflection to approximately\n",
        "**-60 dB**."
      ],
      "id": "4af66add-5619-47de-ab11-8cb4bf545f80"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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M9qFDh0rX7c6dOycdd+jQocRjTk1NhZ+fn8v1atWqOXw6wFnHjh3x2GOPYf369diwYQM2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UuXAAA1a9ZE165dPZ7Tpk0bVKtWTay6HzBgQL4vGJuTRx55BDVq1MD58+fF976oYnsZIiIiIiK6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frk8++URr167V1q1bdfHFF2vFihXq3LlzTS0RAAAgzDR9KvIdCYboJT3VC0KV6t5D\nMuWrxhXYFG9vpURnOyU42irB0VaJjvbB+20U52glu0FrPgAAAACoa2okdP/iiy/Cgfttt92m6dOn\nq0mTJmXGTZs2TW+//bbuvPNOHT9+XL/61a/0zjvv1MQSAQBAI2OafhVFVKoXevcH27+E+qoflClv\nNa7AULy9ZTBEbxcRrrdXoqOt4h2t2ZwUAAAAAOqhGgnd33jjDUnS8OHDNWvWrAr7ht50003yer0a\nP368/ve//yk/P19JSUk1sUwAANDAeP35KvDsVYF3rwq8+5RXvEf2Lj/KcGXqywN/kilPtV4/zt6i\nJFR3tFOCo50SnG2V6GineEcbKtUBAAAAoAGqkdB969atkqSf/OQnldqoa9y4cfrZz36mjIwM7dmz\nR6eddlp1LxEAANRDpulXke+wCjz7VODdFwzY96kweN/tzyxzji0leK4F13fZminBGRGol2oBY7fF\nW3AVAAAAAEB9UiOhe3x84BfODh06VGq8zWZTu3btlJGRoYSEhOpcGgAAqOO8/oJAkF4qWA8956/G\nanWXLa1U+5dQsB64tdv4OQUAAAAAEK1GQvczzzxTn376qfbs2aOhQ4eecLzP59O+ffuUlpam9u3b\n18AKAQBAbTFNv4p9xyIC9b3ByvVAuO72Ha+2azttTZQQrEwPBekllett5bDR4g4AAAAAcHJqJHS/\n7777NH36dL344osaO3bsCVvMvP7668rMzNSkSZPkcNTIEgEAQDXy+YtU4N2vAu/eYMX63pKWMN59\n8pvF1XJdQw4lONopydlBCY72ijPaaOOaIzLdaRoxbIwS49Or5boAAAAAgMarRhLtHj16aPbs2br1\n1ls1YcIEvfDCC0pNTY059vXXX9d9992nq6++WpMnT66J5QEAgFNkmqbc/uMlFeqlWsEU+45U27Wd\ntlQlOtor0dleiY4OEbcdFG9vKcOwh8e63W5tyPlYkuQINXcHAAAAAMBClobuH3zwgY4ePVru8fvv\nv18vvPCC5s2bp5EjR6p///5KS0tTUVGRdu7cqQULFmjz5s0aOHCgRo8erVmzZunaa69V8+bNrVwm\nAACoAtP0q8h7SHneXcr37FaBZ294w9IC7z75zMJqua4huxIcbZTo6KCEiEA9FLQ7Cc8BAAAAAHWI\npaH7lClTtHLlyhOOy83N1VtvvaW33nor5vEVK1ZoxYoVkqT+/fsTugMAUIM8vhzle3cr371TeZ5d\nyvfsUr53l/I9e+Q3i6rlmg5bSiBMd7SPCtQTHR0U72gtm0G7OQAAAABA/cBvsAAANEJ+06MCz17l\ne3Yr3xMK1wP33f6MariiTQmO1oEwPVixnhRRue6yN62GawIAAAAAUPMsDd0XLVokn89n5ZRKTEy0\ndD4AABoL0zRV7DsWqFSP+Mrz7FKhd59MWfv/bLuRWFKlHq5YD/RYT3C0lc1wWno9AAAAAADqIktD\n94SEBCunAwAAleD1FwSr1HeV+totr5ln6bWctiZKcnZRkrOTkhydoirWXbY0GYZh6fUAAAAAAKhv\n6mx7mT179mj27NkaN26cunfvXtvLAQCgVpmmT4XeA8r37FaeZ2dUuF7kO2zptQw5lOTsqCRn5zJf\nBOsAAAAAAFSsToXuBQUFeu+99zRr1ix9+eWXMk1TV1xxRW0vCwCAGuP2ZSnfs7NUuL5bBd498ptu\nS68VZ2+pZGdnJTo7KdnZJRysB1rB1KkfEQAAAAAAqDfqxG/Uixcv1qxZs/T2228rNze3tpcDAEC1\nCmxiuiccqgc2MQ0E7R5/lqXXshsJwXYwnZUUFa53ksOWZOm1AAAAAABALYbuofYxr776qrZt2xZ1\nLCUlRdddd50mTJigs88+u5ZWCADAqfGbnkDFunubcj3blefZoTz3duV7dsmU18Ir2ZToaBfVBiY5\n2Hc9zt6SdjAAAAAAANSgGg3dY7WPiXTJJZdo4sSJuvbaa9mUFQBQb/j8xcr37lKee7vyPNuVG7wt\n8OyRKZ9l13HZ0sr0WE92dlaCs4Pshsuy6wAAAAAAgKqrkdC9ovYxHTp0UG5urrKysvT0009r4MCB\nNbEkAABOms9fqDzPLuV5tivPvU15nh3KdW9XgXevJL8l17AZLiU6Oik52AImKaLXusve1JJrAAAA\nAACA6lNtoXtF7WOaNWumG2+8UePHj9cFF1ygQYMGaeXKldW1FAAATorXXxBuBRO43RaoXPful2Se\n8PzKiLO3ULKzq5KcXYIBe6AdTIKjjQzDbsk1AAAAAABAzbM8dJ87d65eeOGFMu1jEhISNGrUKI0f\nP15XXnmlnE6n1ZcGAOCkePx5ynfvCPZb3x5uD1PoPWDZNeLtrZTs6qZkZ3eluLoq2dlNyc6uclK1\nDgAAAABAg2R56P7UU0+Fq9btdrsuvfRS3XLLLbruuuuUkpJi9eUAADghjy8nEKp7tivXvUN5nm3K\nc29Xke+wZddIcLQNBOqubkp2dlOKs5uSXF3ltCVbdg0AAAAAAFD3VWtP9wkTJujhhx9Wv379qvMy\nAABIkty+rKiK9dCGpsW+oxZdwVCio10wXO+uZGfXYMjeVQ5bokXXAAAAAAAA9ZnloXtaWlr4/quv\nvqpXX31Vp59+usaPH69bbrlFnTt3tvqSAIBGxu3LVG6wz3pJwL5Nbn+GRVewKdHRQSnBQD0QsHdT\nsrOz7LYEi64BAAAAAAAaIstD908++URffPGFZs2apblz56qwsFAbNmzQ73//e/3+97/X0KFDNX78\neN10001q3ry51ZcHADQgHl+Ocj3blOfeFrzdrlzPNrl9xy2Z35Bdic6OgXYwwbYwya5uSnJ0lt0W\nZ8k1AAAAAABA42J56G6z2TRixAiNGDFCOTk5euuttzRr1iwtXbpUkrR06VItXbpUDz30kC677DLd\ncsstKi4utnoZAIB6xOPPC1asbwu0hHFvVa5nu4p9RyyZ35BDSc7O4VYwKcHK9SRnJ9kMNvYGAAAA\nAADWqdae7k2aNNE999yje+65R1u3btWsWbP02muvae/evfJ6vZo/f77mz58fHr9jxw4NHDiwOpcE\nAKhFXn+B8jw7oivX3dtU5Dtoyfw2OZXk6hJsBROoWk9xdlOiswPhOgAAAAAAqBHVGrpH6tGjh556\n6ilNmTKlTPuZkLFjx+qPf/yjJkyYoPHjx6tLly41tTwAgIV8/iLleXYGK9dD7WG2q9C7X5J5yvMH\nwvWuSnF2V7Krq5Kd3ZXi6q4ERzvZjBr7XxsAAAAAAEAZNZ5MnKj9zObNm/WHP/xBf/zjHzV06FC9\n+eabat++fU0vEwBQCT7TrXzPrkCo7t4Wbg9T4N0ryX/K84fawgT6rfcI3nYPVq4TrgMAAAAAgLqn\nVhOLitrPmKapJUuW6NChQ4TuAFDL/KZH+Z49wVB9a3hD0wLPHpnynfL8oQ1NA5Xr3YK3PZTk7Ehb\nGAAAAAAAUK/UmTLB8trPAABqjt/0qsCzt2RD02B7mHzPLpnyWnAFQ4mODsGNTAMtYZJd3ZTk7CK7\n4bJgfgAAAAAAgNpVZ0L3kNLtZ2w2W20vCQAaJLcvS8eLVsnWYqGM+MP69sirKvDukt90WzJ/gqO9\nUpzdlOzqHrztoWRnF9lt8ZbMDwAAAAAAUBfVudA9UpMmTWp7CQDQIHj9+couXq9s93plF69TVvF6\nFXr3SZLsbQJj8jxVmzve3jpYsd493B4m2dlNDluiRasHAAAAAACoP+p06A4AOHk+f5Fy3JuUXbxO\n2e71yipep3zPLknmKc0bZ29ZUrke3Ng02dVNTluyJesGAAAAAABoCAjdAaAe85se5bq3BgL24vXK\ncq9TnnvbKW1u6rI1K1W5HmgP47Q3tXDlAAAAAAAADROhOwDUE6bpU55nZ1TAnuveXOUe7IbsSnJ2\nV86hFJlFbTS432ilJfaWy55m8coBAAAAAAAaD0J3AKiDTNNUgXdvsP96IGTPcW+Qzyys4oyGkp1d\n1DTuDDV19VHTuD5q4uoln9emj1d+LElKixsgl91l3YsAAAAAAABohAjdAaCWmaapIt+hqIA9271B\nXn9OledMdLRX07g+4ZC9SdxpMXuv+1S1KnkAAAAAdYRpyunIV1LccRnuzyXXyNpeEQA0eoTuAFDD\nin3HAu1hQgF78Tq5/RlVni/e3ioQroer2M+Qy55q3YIBAAAA1CrT9Ev+w5J3j+TbI9MXuJV3j+y+\nPbpiQG5gYJ5kJl0sw4iv3QUDQCNH6A4A1cjjy1a2e0NUwF7kO1Tl+Vy2tJIK9rg+auo6Q/GOFhau\nGAAAAEBtME235NsbDNb3RgXr8u2TyvmUqlH6Cd8+ydG9upcLAKgAoTsAWMTrL1COe2MwYA+E7AXe\nPVWez2Ekl4TrcWco1dVH8Y42MowyP1YDAAAAqAdMf24gSA+G6YFgfa/k3S35D0kyT/0i3j2E7gBQ\nywjdAaAKTNOnXM92ZRWtUVbxKmUVr1OeZ4ckf5Xmsxnxauo6LaqKPcnRUYZhs3bhAAAAAKqNaZqS\n/6jk2y15I6rVQxXrZla1XNfnd6igKF1JTU+TPcZeTgCAmkXoDgCV4PZlK6t4jbKKVyuzaJWyi9fK\na+ZXaS6bnEpx9YqqYk92dpXN4FsyAAAAUNeZpkfyHQiH6aY3FKzvDXyZhdVzYaOpZO8gOTpJ9o4y\n7B0lR0d5/K318ScrJNl05ZVXyuFyVc/1AQCVRsIDAKWUVLGvVmbxamUVr1a+Z2eV5jJkV7Krm5q6\nSgL2FFdP2Q1+EAYAAADqKtOfHwzRdwf6q3sjKtZ9ByX5qufCttZSMEw37B3C92XvKMPWNPY5brck\nPiELAHUJoTuARs/ty4qoYl99SlXsSc4uauo6XU3j+ig1ro+auHrLbkuweMUAAAAATpVpeoNtX7ZL\n3u0yvTtKgnX/sWq6qlOytw9XrBv2jsFgvYNk7yDDiKum6wIAahKhO4BGxTR9ynVvVVbxmogq9l1V\nmstpS1VqXF+lxfdTalw/NY07Q05birULBgAAAHBKTLNQ8u4MBuuBgF2+7YHNS+Wx/oJGciBID4bp\nJcF6R8nWWoZht/6aAIA6hdAdQIPm9mUGAvaiQMCeVbxWPrOgCjPZlOLqobS4QMCeGt9PSY5OMgzD\n8jUDAAAAOHmmPyuiaj0Uru+QfPslmdZezNYiUK1u7yQj2P4lXLFupPF7AgA0coTuABoMv+lVnntb\nuII9s2i1Cry7qzSX05YaCNiDVeypcX3ksCVZvGIAAAAAJ8M0Tcl/OEa4vl3yH7fwSnbJ3i4cphuO\nDiXBur2DDFuihdcCADQ0hO4A6i23LzMQsEdVsRdWYSabmrh6KjWur1Lj+iktvr8SHR2pTgEAAABq\nSaDf+t7ocN23XfLukKq4/1JMthaSo5vk6CrD3k1ydAkG621lGEQmAICq4f8gAOqFkir2VRFV7Huq\nNJfLlhasYO+rtLj+ahrXRw4qVQAAAIAaV7bf+o5guL5L1vVbNwKblzq6SfZuMhzdSoJ2W1OLrgEA\nQAlCdwB1UrEvQ1nFa5RVtEqZxauVXbzuFKvY+wXbxfRXoqMDVewAAABADaqZfuvOQKW6o5tk7xoR\nrneRYcRbdA0AAE6M0B1ArfObXuW6twYr2AOV7AXevVWaq6SKPRCyU8UOAAAA1Iwa67duJMWoWu8m\n2dvTEgYAUCfwfyMANa7IezhQxV68RplFa5Tj3lClKnZDdqWEqtiDQTtV7AAAAED1Mk2f5NsXEa5v\nC7aE2V4N/da7BsP1riXhuq0VP/MDAOo0QncA1crnL1K2e0Ngo9OitcoqXq0i3+EqzeWyNVNqfLBN\nTFxfqtgBAACAamSabsm7W/JtKwnXvdsDPdjltugq9FsHADQ8hO4ALGOapgq8e5RVtFqZwUr2XPcW\nmfKe9FyBKvZewT7sfaliBwAAAKqJ6S8I9Ff3bitpCePdLvn2SPJZdBX6rQMAGg9CdwBV5vHlKKt4\nbbhVTFbxGnn82VWay2VPV2pc32AVez81jTuDKnYAAADAQtGbmW4rCdf9B6y7CP3WAQAgdAdQOX7T\nqzz3NmUWrw4G7auV79lZpblscqpJ3OlKjesb/kpwtKWKHQAAADhFgc1Mj0RvZhrqt+4/Zt2FbM0k\nezfJ0T06XKffOgAAhO4AYivyHg30YQ9WsGcXr6/SZqeSlOhor6ZxfZUW11ep8f2U4uolu+GyeMUA\nAABA42Gafsm3X/Juk3w7gm1hgtXrZq51F7K1CQfqkeG6YWtm3TUAAGhgCN0ByOcvUo57Y7gPe1bR\nahX5DlVpLoeRpKZxZwYq2OP7KjXuTMXZ0y1eMQAAANA4mKZH8u2Orlz3bgtuZlpk0VVskr2D5Oge\n2MDUEahgl72rDFuyRdcAAKDxIHQHGpnAZqd7g1Xsa5VVtFo57s1V2uxUMpTs7B6sYA+0iUl2dpVh\n2C1fNwAAANCQmaY7EKR7t8r0bo3YzHS3VKWf1WOJ3My0W0m47ugsw4iz6BoAAIDQHWjgPP7cYLge\nudlpVpXmctmaKTW+n1Ljzgxvduqk8gUAAACotEBbmH2Sd0swYN8sebcGK9ctCteNRMneNdgSpnuw\nJUx3NjMFAKCG8H9boAExTZ9yPduCAXugH3ueZ6ck86TnCmx2elqpzU7bsSkSAAAAUAmBDU2PBcP1\nLcHq9c2B1jBV3CupDCM1ot9694jNTFvLMGzWXAMAAJw0QnegHjNNU7meLTpW+K2OFy5TRtHKKm92\nmuBoFw7X0+L6KSWuN5udAgAAAJVg+nOD1epbZAYr2OXZLJlZ1lzA1jLYBiaiJYy9m2RrRlEMAAB1\nEKE7UM8Ueg/peOG3Ola4TMeKvpXbd/yk57AbicEWMYE2ManxZyrO3rwaVgsAAAA0HKZZHOy1vjUY\nrm+RPFsl/wFrLmBrLTl6SI6eUZXrhi3FmvkBAECNIHQH6jiPP08Zhd/rWNEyHStcpnzPzpOcwVCy\ns1uggj2+r5rG9VWKsxubnQIAAADlME2f5NtT0nfdEwzYfbsl+U79AkZTydFTcvaU4ehZErTbmpz6\n3AAAoNYRugN1jN/0KKt4rY4VLtXxwm+VVbxW5kn8YO+yNQtUsMf3DW522ofNTgEAAIAYAn3XD5cN\n173bJBVbcIX4QCuYcLgeDNhtLWkLAwBAA0boDtQy0zSV59kRDtmPF30vn1lQ6fPtRqLS4wepecK5\nSk84V8nOrvwADwAAAJRi+rNLNjX1bA0H7TKzLZjdLjm6SI4eEeF6T8nenk+YAgDQCBG6A7WgyHsk\n0Je9aJmOFX6rYt/RSp9ryK7UuDOVnnCumiecq9S4M2UznNW4WgAAAKAeMQvVNHGfUhIPyVawTv68\nHZJ3s+Q/Ys38tnaSs2epgL2rDMNlzfwAAKDeI3QHaoDXn6+MohWBzU8Lv1WeZ9tJnZ/k7KLmCeeq\nefy5apYwiHYxAAAAaPRM0yN5dwUq170llet23x5deKYZGFR0Chcw0iRnr+Cmpj3CrWEMfhYHAAAn\nQOgOVAO/6VV28bpwyJ5VvFqmvJU+32VPV/P4c4ItY85RgqN1Na4WAAAAqLsCm5ruK+m7Huq57t0p\nyVNm/Ek3WjQSgxuZ9pDh6BW830uGPd2K5QMAgEaI0B2wgGmayvfs0rGiZYG+7IXL5TXzKn2+3UhQ\ns/iB4ZA9xdmDvuwAAABoVGJvaro1uKnpqZSshzglR9dSbWF6SPZ2MgybBfMDAAAEELoDVVTsO65j\nhd/qeLCavch36CTOtik1rk8gZI8/R2nx/enLDgAAgEbD9GdIni0RrWGCX2buqc8tQwVFzZRb2Eot\n25wve9xpwYC9E33XAQBAjSB0ByrJ5y9URtHKQMuYomXKdW85qfMTHZ0CfdkTzlF6/GA57U2qaaUA\nAABA3WD684KBenTfdfmPW3MBW6uSXuuOnpKzh7z+DvpywUJJ0pXdrpTDRdAOAABqFqE7UA7T9Cnb\nvSHYl32ZsopWyR+jZ2R5XLY0pSeco+YJ56h5/LlKcLatxtUCAAAAtcc0iyTv9pJw3RPsu+4/YM0F\njNTgpqY9gpuaBnuw25qWHet2W3NNAACAKiJ0B4JM05Rcx2VL3qbVx79QpnuFvP6cSp9vM+LULH5A\noJo9/lyluHrSGxIAAAANiml6JN9uyVOqct23R5L/1C9gJEVsahrsue7oKdnS2fMIAADUG4TugCSP\nP09LDl8vZ+9AJc7RSu3TZKip6/Tg5qfnKi2uv+y2uGpdJwAAAFATTNMv+faVbGoa7ru+QzqJT3+W\nzyU5ukuO7iXhurOnZGtLuA4AAOo9QndAktOWrB72TCU7Kv4FwmY45TCS5bAlyWFLkiG35F8o5S+U\n8iW/6sAvCIZTklMyHBH3I28dpW5dMgxHjOPBx7GeK29s6LoRj/mlCQAAoG4zTXegFYxno0zvBsmz\nQfJuksx8C2a3S/bOkrNnRFuYnpK9Q/BnUAAAgIaHn3KAoCb2JKUo+wSj3JIyJH+GJZ+erSvMap27\ndMjvkAyXygb0DslIkIxkyZYsGSkygrcyUoLPJUu2lMBt6DnFE+wDAABUkunPl7ybJc96md6NwYB9\nqyypXre3j97U1NFDcnSVYbCRKQAAaFwI3YEgp62pdRs9IYJXMr2BuyeZ7lduuENmRFAfK6A3jJSI\nsD5ibMRzhmE/ydcFAABQt5n+DMkTCNbDFey+XTrlkgtby+hw3dlDsneTYUuyYtkAAAD1HqF7HZab\nm6sNGzbo+PHjcjqdatOmjXr37i2H4+T+tW3cuFH79u1TXFycunfvrrZt21bTius3ly2tQVWvNx5e\nycySfFnljqjMr5WmkRTYuCsyjI8M5UtX2IePh8Y2kWHQ0x8AANQ80zQl/8FA9bpngxSqYPcfOrWJ\njSaSo5fkjKxc7yHDlmrJugEAABoqQvc6aOXKlZo0aZI+++wz+Xy+qGNNmjTRhAkT9Pjjj6t58+YV\nzjN79mw9/vjj2rFjR/g5wzB0wQUX6O9//7v69+9fHcuvt/xJv9fixZ9KkoaeN1ROp/MkZ7CiScsp\nzmGaKqks90imJ+LWW+pxyfNm1HF34PzwmMrO5Y19vL4w84N9S49IvhiHKzOFXJKtaTCYbyrZmgR+\nWbU1kYymweC+afixbMFxRlPJSKJNDgAAOCHT9Em+nYH+654NkndDoJrdzDq1iW2tJOfpkuN0Gc7T\nJMfpkr0dP58AAABUAaF7HTN37lzddNNN8noD7Tg6deqk7t27q6CgQBs3blRWVpb++c9/6sMPP9Si\nRYvUsWPHmPM8+uijevbZZyVJqampGjBggPLz8/X999/rm2++0eDBg/XRRx9pxIgRNfba6jx7J2UX\ntA/cd5whw9l4ek9W169SZviPABWH/iWPCyR/nmTmSmaezNB9f+CxzLyI+8Hn61Sw75b8RyUdrUJw\nb5MZDuiDt7am4fuGEf04Krg3mrARGQAADZBpFkveLYH2MJ6NgYDdu1kyC09hViO4selpMhynB4J2\n5+kybM2sWjYAAECjR0pTh+Tk5Oiuu+6S1+tVQkKCXn/9dV177bXh4wUFBfrDH/6gv/3tb9qzZ49+\n9rOf6YMPPigzz7vvvhsO3MeMGaN///vfSkoK9FfcvHmzrr76am3btk1jxozRli1b1LJlyxp5fWh8\nApVRoY1Sq3B+JcaYprtsEB++nxcM73PDQX7ZAD83WOFe2/wVtsk5UaV9oD1OZBhfEuAbkdX3ofA+\nFO6bCVa/EAAAUAWmPy/cFiZcwe7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          }
        }
      ],
      "source": [
        "import matplotlib.cm as cm\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "# Normalize the colormap based on the minimum and maximum values in the num_layers_list\n",
        "norm = mcolors.Normalize(vmin=min(num_layers_list), vmax=max(num_layers_list))\n",
        "colormap = cm.viridis  # Use the 'viridis' colormap for plotting\n",
        "\n",
        "# Iterate over each number of absorber layers in the list\n",
        "for num_layers in num_layers_list:\n",
        "    # Calculate the reflection values by taking the absolute square of the mode amplitude values from the simulation results\n",
        "    reflection = (\n",
        "        np.abs(batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\").values)\n",
        "        ** 2\n",
        "    )\n",
        "    # Get the color corresponding to the current number of layers using the normalized colormap\n",
        "    color = colormap(norm(num_layers))\n",
        "    # Plot the reflection values in dB against the wavelengths, using the specified color and label\n",
        "    plt.plot(ldas, 10 * np.log10(reflection), color=color, label=f\"{num_layers} layers\")\n",
        "\n",
        "\n",
        "plt.ylim(-100, 0)\n",
        "plt.xlabel(\"Wavelength (μm)\")\n",
        "plt.ylabel(\"Absorber reflection (dB)\")\n",
        "plt.legend(loc=\"upper left\", bbox_to_anchor=(1.05, 1), borderaxespad=0.0)\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "id": "d500c7ae"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As discussed, increasing the number of layers can help reduce unwanted\n",
        "reflections, but it may also significantly raise the cost. In the\n",
        "accompanying plot, we show the reflection at the central wavelength\n",
        "(1550 nm) alongside the number of FDTD cells. In very small simulations,\n",
        "such as this benchmark model, using more absorber layers might not\n",
        "increase the cost since all simulations remain below the minimal cost\n",
        "threshold. However, for larger simulations, the cost difference between\n",
        "using 100 layers and 40 layers can be substantial. Users must balance\n",
        "accuracy and cost based on their objectives and tolerance levels."
      ],
      "id": "fc39f972-fa11-4019-b1b5-29f5d4d5a7f0"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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5Dh+WHnxQmjIl7b5cuaSXXrLmVf5G0hAIffv3S5MmWQJx1ar0H1ehgtVAvOMO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1Dxw0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\nhgNkWyQNAaTd5s2WLHz5ZSkhwfuYAgVs9uGkSVLhwq6GBwAAACBnePhh6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          }
        }
      ],
      "source": [
        "# Calculate the reflection values at the central frequency for different numbers of absorber layers\n",
        "reflection_freq0 = [\n",
        "    np.abs(\n",
        "        batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\", f=freq0).values\n",
        "    )\n",
        "    ** 2\n",
        "    for num_layers in num_layers_list\n",
        "]\n",
        "\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "# Plot the reflection values in dB against the number of absorber layers\n",
        "ax1.plot(num_layers_list, 10 * np.log10(reflection_freq0), color=\"b\", linewidth=2)\n",
        "ax1.set_xlabel(\"Number of absorber layers\")\n",
        "ax1.set_ylabel(\"Reflection at central frequency (dB)\", color=\"b\")\n",
        "ax1.tick_params(axis=\"y\", labelcolor=\"b\")\n",
        "\n",
        "# Create a second y-axis sharing the same x-axis for additional data visualization\n",
        "ax2 = ax1.twinx()\n",
        "# Plot the number of FDTD cells (in millions) against the number of absorber layers\n",
        "ax2.plot(num_layers_list, num_cells_list, color=\"r\", linewidth=2)\n",
        "ax2.set_ylabel(\"Number of FDTD cells (million)\", color=\"r\")\n",
        "ax2.tick_params(axis=\"y\", labelcolor=\"r\")\n",
        "\n",
        "plt.show()"
      ],
      "id": "1e00a3cf"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Reflection as a Function of Grid Resolution\n",
        "\n",
        "Next we are going to test how the reflection depends on the grid\n",
        "resolution and thus the physical layer thickness of the absorber region.\n",
        "Similarly we perform a parameter sweep from 10 to 30 minimum steps per\n",
        "wavelength."
      ],
      "id": "3ee1be75-a426-490e-8a8c-78b1efe37242"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:34 UTC </span>Got <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> simulation from cache.                                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"41e577c068fd4a59a1cc1a10ae2d8e8c\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:37 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:41 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.108</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"0ba277e543844c41a7f9c97c3085827d\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:43 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "grid_resolution_list = list(\n",
        "    range(10, 31, 5)\n",
        ")  # Create a list of absorber layer counts from 10 to 100 in increments of 10\n",
        "\n",
        "# Initialize an empty dictionary to store simulation objects with their respective layer counts as keys\n",
        "sims = {}\n",
        "\n",
        "# Loop over each number of absorber layers to create simulations\n",
        "for grid_resolution in grid_resolution_list:\n",
        "    sim = make_sim(num_layers=60, grid_resolution=grid_resolution)\n",
        "    # Append the number of cells (converted to millions) to the num_cells_list\n",
        "    sims[f\"{grid_resolution} steps per wavelength\"] = sim\n",
        "\n",
        "# Create a batch of simulations\n",
        "batch = web.Batch(simulations=sims, verbose=True)\n",
        "\n",
        "# Run the batch of simulations and save results to the specified directory\n",
        "batch_results = batch.run(path_dir=\"data\")"
      ],
      "id": "9264272e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plotting the results in a similar way, we can clearly see that the\n",
        "reflection increases with grid resolution since, for the same number of\n",
        "layers, the physical thickness of the absorber region decreases with\n",
        "resolution. Therefore, for simulations with a very fine grid, a larger\n",
        "number of absorber layers is needed."
      ],
      "id": "916683db-241a-4e5f-bbc5-bedf941a7ab4"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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bW1vD1dUV1tbWVXaOsl5TBwcHuLq6on79+irL/fz8cPfuXaxatQqhoaGQyWRw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          }
        }
      ],
      "source": [
        "# Normalize the colormap based on the minimum and maximum values in the grid_resolution_list\n",
        "norm = mcolors.Normalize(vmin=min(grid_resolution_list), vmax=max(grid_resolution_list))\n",
        "colormap = cm.viridis  # Use the 'viridis' colormap for plotting\n",
        "\n",
        "# Iterate over each grid_resolution in the list\n",
        "for grid_resolution in grid_resolution_list:\n",
        "    # Calculate the reflection values by taking the absolute square of the mode amplitude values from the simulation results\n",
        "    reflection = (\n",
        "        np.abs(\n",
        "            batch_results[f\"{grid_resolution} steps per wavelength\"][\"modemonitor\"]\n",
        "            .amps.sel(direction=\"-\")\n",
        "            .values\n",
        "        )\n",
        "        ** 2\n",
        "    )\n",
        "    # Get the color corresponding to the current grid_resolution using the normalized colormap\n",
        "    color = colormap(norm(grid_resolution))\n",
        "    # Plot the reflection values in dB against the wavelengths, using the specified color and label\n",
        "    plt.plot(\n",
        "        ldas,\n",
        "        10 * np.log10(reflection),\n",
        "        color=color,\n",
        "        label=f\"{grid_resolution} steps per wavelength\",\n",
        "    )\n",
        "\n",
        "\n",
        "plt.ylim(-100, 0)\n",
        "plt.xlabel(\"Wavelength (μm)\")\n",
        "plt.ylabel(\"Absorber reflection (dB)\")\n",
        "plt.legend(loc=\"upper left\", bbox_to_anchor=(1.05, 1), borderaxespad=0.0)\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "id": "0f0ffe30"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## PML\n",
        "\n",
        "For comparison, we conducted the same simulation using the PML boundary\n",
        "condition. In Tidy3D, the default number of PML layers is 12, which\n",
        "typically performs very well. Increasing the number of layers generally\n",
        "does not provide additional benefits. In our study, we varied the number\n",
        "of PML layers from 10 to 20, increasing in increments of 2."
      ],
      "id": "f82bb217-1d47-4bc8-8586-c4b6249494c9"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"1cdc4bc0648d41d4ac0e9953876c70fc\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:48 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:54 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.150</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"30d326d4a21e41408c51ff1d0f539c45\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">01:03:57 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "# Create a list of even numbers from 10 to 20, representing the number of PML layers for simulations\n",
        "num_layers_list = list(range(10, 21, 2))\n",
        "\n",
        "\n",
        "sims = {\n",
        "    f\"{num_layers} layers\": make_sim(num_layers, use_absorber=False)\n",
        "    for num_layers in num_layers_list\n",
        "}\n",
        "\n",
        "\n",
        "batch = web.Batch(simulations=sims, verbose=True)\n",
        "\n",
        "batch_results = batch.run(path_dir=\"data\")"
      ],
      "id": "50cefa41"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The plot clearly demonstrates that PML with 12 layers outperforms the\n",
        "adiabatic absorber with 100 layers, highlighting the superiority of PML.\n",
        "The reflection is already at the level of numerical noise, so users can\n",
        "typically rely on the default settings."
      ],
      "id": "94e6ae87-d8c6-49b9-b43d-4120ebd8d1b2"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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NgQdAed4Ls6HqrbF4oHEIgG4FZcG33pRES0BTAw30QOHjxYW//Qhz7UEJZX7d27F29961tx9913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\n0WsVDHqc8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o+rHR65HS65A4rUDo/cBZ8riqjo2/A+70REtHU1PTB/8skn8eUvfxn/7//9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          }
        }
      ],
      "source": [
        "norm = mcolors.Normalize(vmin=min(num_layers_list), vmax=max(num_layers_list))\n",
        "colormap = cm.viridis  # Use the 'viridis' colormap for plotting\n",
        "\n",
        "for num_layers in num_layers_list:\n",
        "    reflection = (\n",
        "        np.abs(batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\").values)\n",
        "        ** 2\n",
        "    )\n",
        "    color = colormap(norm(num_layers))\n",
        "    plt.plot(ldas, 10 * np.log10(reflection), color=color, label=f\"{num_layers} layers\")\n",
        "\n",
        "plt.ylim(-130, 0)\n",
        "plt.xlabel(\"Wavelength (μm)\")\n",
        "plt.ylabel(\"PML reflection (dB)\")\n",
        "plt.legend(loc=\"upper left\", bbox_to_anchor=(1.05, 1), borderaxespad=0.0)\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "id": "910acaf7"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For PML, the reflection shouldn’t depend noticeably on the grid\n",
        "resolution so we can skip the corresponding test here.\n",
        "\n",
        "## Final Remarks\n",
        "\n",
        "This notebook illustrates the differences in reflection between\n",
        "adiabatic absorbers and PML, emphasizing how the number of layers\n",
        "influences performance. Reflection from the absorber can arise from two\n",
        "main sources. The first, and more common, issue is that the conductivity\n",
        "does not increase gradually enough. This can be addressed by increasing\n",
        "the number of absorber layers, which is set to 40 by default. The second\n",
        "issue occurs when the absorption is insufficient, allowing light to\n",
        "reach the Perfect Electric Conductor (PEC) boundary at the end of the\n",
        "absorber and reflect back without being fully attenuated before\n",
        "re-entering the simulation region. This would manifest itself as the\n",
        "reflection level not decreasing when more layers are added. In such\n",
        "cases, increasing the maximum conductivity (refer to the [API\n",
        "documentation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AbsorberParams.html))\n",
        "can be beneficial. In some cases, it could be possible to decrease\n",
        "`sigma_max` instead of increasing `num_layers`, if the absorption stays\n",
        "strong enough while becoming more gradual due to the lower maximum\n",
        "value. Additionally, altering the conductivity scaling order\n",
        "(`sigma_order`) might have an impact, though this is an advanced setting\n",
        "that we typically do not recommend adjusting.\n",
        "\n",
        "In summary, PML is highly preferred as the boundary condition when the\n",
        "simulation does not diverge. When absorber boundaries need to be used,\n",
        "users are encouraged to experiment and benchmark the reflection for the\n",
        "particular simulation setting they are interested in by following a\n",
        "similar procedure outlined in this notebook to achieve a balance between\n",
        "accuracy and cost."
      ],
      "id": "9839cbc8-dd51-4a2b-a84b-5a8913e28539"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
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