{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed270d92",
   "metadata": {},
   "source": [
    "# Exceptional coupling for waveguide crosstalk reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0127a34b",
   "metadata": {},
   "source": [
    "**This notebook contains large simulations. Running the entire notebook will cost about 20 FlexCredits.**\n",
    "\n",
    "The current technological trend emphasizes the dense integration of photonic components on a chip. However, as the proximity between these components increases, the issue of crosstalk becomes more significant and detrimental. Therefore, we need to apply other design ideas to actively minimize the crosstalk.\n",
    "\n",
    "In this notebook, we investigate the crosstalk between two strip waveguides, which are fundamental photonic components. We demonstrate that the utilization of anisotropic metamaterial (also known as subwavelength grating (swg)) cladding enables remarkable reduction of crosstalk by leveraging the exceptional coupling as proposed in the article `Md Borhan Mia, Syed Z. Ahmed, Ishtiaque Ahmed, Yun Jo Lee, Minghao Qi, and Sangsik Kim, \"Exceptional coupling in photonic anisotropic metamaterials for extremely low waveguide crosstalk,\" Optica 7, 881-887 (2020)` [DOI: 10.1364/OPTICA.394987](https://doi.org/10.1364/OPTICA.394987). The waveguide with anisotropic metamaterial cladding is shown to have an extreme skin depth and thus it's termed eskid waveguide. The reduced skin depth leads to reduced crosstalk. Moreover, at a certain wavelength, exceptional coupling occurs where the symmetric and antisymmetric modes have identical effective index and the coupling is zero in principle. \n",
    "\n",
    "<img src=\"img/exceptional_coupling_te.png\" width=\"500\" alt=\"Schematic of the anisotropic metamaterial for crosstalk reduction\">\n",
    "\n",
    "For more integrated photonic examples such as the [waveguide crossing](https://www.flexcompute.com/tidy3d/examples/notebooks/WaveguideCrossing/), the [strip to slot waveguide converter](https://www.flexcompute.com/tidy3d/examples/notebooks/StripToSlotConverters/), and the [broadband directional coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/BroadbandDirectionalCoupler/), please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ebd0f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdstk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d700682",
   "metadata": {},
   "source": [
    "## Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41384f07",
   "metadata": {},
   "source": [
    "For the simulation, we will investigate the wavelength range between 1500 nm and 1600 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "109f2ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "ldas = np.linspace(1.5, 1.6, 101)  # wavelength range\n",
    "freqs = td.C_0 / ldas  # frequency range\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bdce7e7",
   "metadata": {},
   "source": [
    "Define materials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed9dd726",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_si = 3.43  # silicon refractive index\n",
    "si = td.Medium(permittivity=n_si**2)\n",
    "\n",
    "n_sio2 = 1.444  # silicon oxide refractive index\n",
    "sio2 = td.Medium(permittivity=n_sio2**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1502bed6",
   "metadata": {},
   "source": [
    "Define geometric parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbd106f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 0.45  # waveguide width\n",
    "h = 0.22  # waveguide thickness\n",
    "p = 0.1  # period of the swg\n",
    "frac = 0.5  # filling fraction\n",
    "N = 5  # number of grating periods\n",
    "L = 100  # length of the coupling region\n",
    "inf_eff = 1e2  # effective infinity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd8b2a9",
   "metadata": {},
   "source": [
    "Define the waveguides and the anisotropic metamaterial cladding. The most convenient way is to use `gdstk`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "325d2e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "R = 10  # radius of the bend\n",
    "\n",
    "cell_eskid = gdstk.Cell(\"eskid\")  # define a cell for the eskid waveguides\n",
    "cell_ref = gdstk.Cell(\"ref\")  # define a cell for bare strip waveguides\n",
    "\n",
    "# define the top strip waveguide\n",
    "path_1 = gdstk.RobustPath(initial_point=(-L / 2 - R, inf_eff), width=w, layer=1, datatype=0)\n",
    "\n",
    "path_1.vertical(y=(N + 0.5) * p / 2 + w / 2 + R)\n",
    "path_1.arc(radius=R, initial_angle=-np.pi, final_angle=-np.pi / 2)\n",
    "path_1.horizontal(x=L / 2)\n",
    "path_1.arc(radius=R, initial_angle=-np.pi / 2, final_angle=0)\n",
    "path_1.vertical(y=inf_eff)\n",
    "cell_eskid.add(path_1)\n",
    "cell_ref.add(path_1)\n",
    "\n",
    "# define the bottom strip waveguide\n",
    "path_2 = path_1.copy()\n",
    "path_2.mirror(p1=(1, 0))\n",
    "cell_eskid.add(path_2)\n",
    "cell_ref.add(path_2)\n",
    "\n",
    "# define the swg between the waveguides\n",
    "path_3 = gdstk.RobustPath(\n",
    "    initial_point=(-1.2 * L / 2, 0),\n",
    "    width=[frac * p, frac * p, frac * p, frac * p, frac * p],\n",
    "    offset=p,\n",
    "    layer=1,\n",
    "    datatype=0,\n",
    ")\n",
    "\n",
    "path_3.horizontal(x=1.2 * L / 2)\n",
    "cell_eskid.add(path_3)\n",
    "\n",
    "# define the swg on the top\n",
    "path_4 = gdstk.RobustPath(\n",
    "    initial_point=(-L / 2 - R / 2, (N + 0.5) * p + w + R / 2),\n",
    "    width=[frac * p, frac * p, frac * p, frac * p, frac * p],\n",
    "    offset=p,\n",
    "    layer=1,\n",
    "    datatype=0,\n",
    ")\n",
    "\n",
    "path_4.arc(radius=R / 2, initial_angle=-np.pi, final_angle=-np.pi / 2)\n",
    "path_4.horizontal(x=L / 2)\n",
    "path_4.arc(radius=R / 2, initial_angle=-np.pi / 2, final_angle=0)\n",
    "cell_eskid.add(path_4)\n",
    "\n",
    "# define the swg on the bottom\n",
    "path_5 = path_4.copy()\n",
    "path_5.mirror(p1=(1, 0))\n",
    "cell_eskid.add(path_5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a35c31e",
   "metadata": {},
   "source": [
    "After the gds cell is created, we can define Tidy3D geometries from the gds cell and visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5be1f5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define tidy3d geometries from the gds cell\n",
    "eskid_geo = td.PolySlab.from_gds(\n",
    "    cell_eskid,\n",
    "    gds_layer=1,\n",
    "    axis=2,\n",
    "    slab_bounds=(-h / 2, h / 2),\n",
    ")\n",
    "\n",
    "# plot the geometries\n",
    "fig, ax = plt.subplots()\n",
    "for geo in eskid_geo:\n",
    "    geo.plot(z=0, ax=ax)\n",
    "\n",
    "ax.set_xlim(-0.7 * L, 0.7 * L)\n",
    "ax.set_ylim(-0.2 * R, 0.2 * R)\n",
    "ax.set_aspect(\"auto\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf8f02f",
   "metadata": {},
   "source": [
    "Define Tidy3D Structures using the previously defined geometries and materials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57593a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define tidy3d structures from the geometries\n",
    "eskid_structure = [td.Structure(geometry=geo, medium=si) for geo in eskid_geo]\n",
    "\n",
    "# define the substrate structure\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -h / 2)\n",
    "    ),\n",
    "    medium=sio2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6ffa64",
   "metadata": {},
   "source": [
    "Define a ModeSource to excite the TE0 mode at the input waveguide and then define two ModeMonitors at the through port and cross port to measure the transmission. And finally we add a FieldMonitor at the $z=0$ plane to visualize the power propagation and coupling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc2509f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a mode source as excitation\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_si)\n",
    "\n",
    "input_x = -L / 2 - R\n",
    "input_y = (N + 1) * p / 2 + w / 2 + R + lda0 / 4\n",
    "\n",
    "mode_source = td.ModeSource(\n",
    "    center=(input_x, input_y, 0),\n",
    "    size=(5 * w, 0, 9 * h),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"-\",\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    ")\n",
    "\n",
    "# add a mode monitor to measure transmission at the through port\n",
    "mode_monitor_I1 = td.ModeMonitor(\n",
    "    center=(-input_x, input_y, 0),\n",
    "    size=(5 * w, 0, 9 * h),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"I1\",\n",
    ")\n",
    "\n",
    "# add a mode monitor to measure transmission at the cross port\n",
    "mode_monitor_I2 = td.ModeMonitor(\n",
    "    center=(-input_x, -input_y, 0),\n",
    "    size=(5 * w, 0, 9 * h),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"I2\",\n",
    ")\n",
    "\n",
    "# add a filed monitor to visualize field distribution at z=0\n",
    "field_monitor = td.FieldMonitor(\n",
    "    size=(td.inf, td.inf, 0), freqs=[freq0], interval_space=(3, 3, 1), name=\"field\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855d7d04",
   "metadata": {},
   "source": [
    "Define a Tidy3D Simulation using the components above. Due to the thin anisotropic metamaterial claddings, a fine grid needs to be used to ensure an accurate simulation. Here we use the automatic nonuniform grid with `min_steps_per_wvl` set to 20. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b9fda89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simulation domain size\n",
    "Lx = 1.25 * L\n",
    "Ly = 2.4 * R\n",
    "Lz = 9 * h\n",
    "sim_size = (Lx, Ly, Lz)\n",
    "\n",
    "run_time = 5e-12  # simulation run time\n",
    "\n",
    "# construct simulation\n",
    "sim_eskid = td.Simulation(\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n",
    "    structures=eskid_structure + [substrate],\n",
    "    sources=[mode_source],\n",
    "    monitors=[mode_monitor_I1, mode_monitor_I2, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    ")\n",
    "\n",
    "# plot the simulation\n",
    "sim_eskid.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d1f132",
   "metadata": {},
   "source": [
    "Since the simulation contains very thin waveguides, it's crucial to have a sufficiently small grid size to resolve them. We can inspect the grid by using the `plot_grid` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "745a7c3f-e067-4a9a-8e83-a2d27b688efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inspect the grid\n",
    "ax = sim_eskid.plot(z=0, hlim=[-1, 1], vlim=[-1, 1])\n",
    "sim_eskid.plot_grid(z=0, ax=ax, hlim=[-1, 1], vlim=[-1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfcab2f",
   "metadata": {},
   "source": [
    "Once everything is confirmed, we submit the simulation job to the server. The fine grid combined with the large simulation domain will lead to a higher FlexCredit cost. Before running the simulation, we can get a cost estimation using `estimate_cost`. This prevents us from accidentally running large jobs that we set up by mistake. The estimated cost is the maximum cost corresponding to running all the time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36ad7d9e",
   "metadata": {},
   "outputs": [
    {
     "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\">12:09:11 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'eskid'</span> with task_id                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:11 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'eskid'\u001b[0m with task_id                                 \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a3-4808-9ca3-778dc32775e7'</span></a>.                                       \n",
       "</pre>\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=491262;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=604736;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=491262;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=208718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=491262;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\u001b\\\u001b[32m-1bcf5fbc-c7\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=491262;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1bcf5fbc-c7a3-4808-9ca3-778dc32775e7\u001b\\\u001b[32ma3-4808-9ca3-778dc32775e7'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=658820;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b15513402a04b5098154be73a829cea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">12:09:13 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.840</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:13 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m13.840\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": [
       "<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\">12:09:14 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.840</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:14 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m13.840\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job = web.Job(simulation=sim_eskid, task_name=\"eskid\", verbose=True)\n",
    "estimated_cost = web.estimate_cost(job.task_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b28918",
   "metadata": {},
   "source": [
    "The cost is reasonable given the simulation size so we can run the simulation now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e01f8fc",
   "metadata": {},
   "outputs": [
    {
     "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\">12:09:15 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:15 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a98929b606549deb072a979671b278e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">12:09:30 CEST </span>loading simulation from data/eskid.hdf5                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:30 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/eskid.hdf5                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_eskid = job.run(path=\"data/eskid.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77750700",
   "metadata": {},
   "source": [
    "## Result Analysis and Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0759e40f",
   "metadata": {},
   "source": [
    "We define a helper function `postprocess` to plot the crosstalk and field distribution. The crosstalk is defined as $I_2/I_1$, where $I_1$ is the power transmitted to the through port and $I_2$ is the power transmitted to the cross port."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b458e31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def postprocess(sim_data):\n",
    "    I1 = np.abs(sim_data[\"I1\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "    I2 = np.abs(sim_data[\"I2\"].amps.sel(mode_index=0, direction=\"-\")) ** 2\n",
    "    crosstalk = I2 / I1\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "\n",
    "    ax1.plot(ldas, 10 * np.log10(crosstalk))\n",
    "    ax1.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
    "    ax1.set_xlim(np.min(ldas), np.max(ldas))\n",
    "    ax1.set_ylim(-80, 0)\n",
    "    ax1.set_ylabel(\"Crosstalk (dB)\")\n",
    "\n",
    "    sim_data.plot_field(\"field\", \"E\", \"abs\", vmin=0, vmax=80, ax=ax2)\n",
    "    ax2.set_aspect(\"auto\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f84d2a",
   "metadata": {},
   "source": [
    "Call the `postprocess` function on the simulation data of the eskid waveguide simulation. Here we see the crosstalk is below -40 dB around the 100 nm bandwidth. At about 1550 nm, exception coupling occurs and the crosstalk is well below -80 dB. The exceptional coupling wavelength can be effectively tuned by changing the geometric parameters such as the waveguide width.\n",
    "\n",
    "From the field distribution plot, we see the field stays at the top waveguide throughout the coupling regime, confirming the low crosstalk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ffb10593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "postprocess(sim_data_eskid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7ff939",
   "metadata": {},
   "source": [
    "## Reference Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00801814",
   "metadata": {},
   "source": [
    "The crosstalk of the eskid waveguide has been shown to be very low. As a comparison, we simulate two strip waveguides with the same spacing without the anisotropic metamaterial claddings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b22a9d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define geometries for the bare strip waveguides\n",
    "ref_geo = td.PolySlab.from_gds(\n",
    "    cell_ref,\n",
    "    gds_layer=1,\n",
    "    axis=2,\n",
    "    slab_bounds=(-h / 2, h / 2),\n",
    ")\n",
    "\n",
    "# define structures for the bare strip waveguides\n",
    "ref_structure = [td.Structure(geometry=geo, medium=si) for geo in ref_geo]\n",
    "\n",
    "# copy the simulation and update the structures to bare strip waveguides\n",
    "sim_ref = sim_eskid.copy(update={\"structures\": ref_structure + [substrate]})\n",
    "\n",
    "# plot simulation\n",
    "sim_ref.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0957074b",
   "metadata": {},
   "source": [
    "submit the simulation job to the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfa71e95",
   "metadata": {},
   "outputs": [
    {
     "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\">12:09:33 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'ref'</span> with task_id                                   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-427ca4f7-e74e-43a1-862f-79f26e156007'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:33 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'ref'\u001b[0m with task_id                                   \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-427ca4f7-e74e-43a1-862f-79f26e156007'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e7</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4e-43a1-862f-79f26e156007'</span></a>.                                       \n",
       "</pre>\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=206166;https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=521226;https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=206166;https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=214939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=206166;https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\u001b\\\u001b[32m-427ca4f7-e7\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=206166;https://tidy3d.simulation.cloud/workbench?taskId=fdve-427ca4f7-e74e-43a1-862f-79f26e156007\u001b\\\u001b[32m4e-43a1-862f-79f26e156007'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=376126;https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83ac07d51df94a139b7ce64e9b0ed13b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">12:09:36 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.282</span>. Minimum cost depends on task     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:36 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m12.282\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": [
       "<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\">12:09:37 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:37 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee77edf1c81e4b51afc70ad5d29c8cf4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\">12:09:54 CEST </span>loading simulation from data/ref.hdf5                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:09:54 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/ref.hdf5                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_ref = web.run(sim_ref, task_name=\"ref\", path=\"data/ref.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6303b887",
   "metadata": {},
   "source": [
    "Apply the same visualization to the simulation data. We can see that the crosstalk is much larger in the bare strip waveguide case, around -10 dB. In the field distribution plot, a noticeable amount of energy is coupled to the lower waveguide. This comparison further validates the effectiveness of the anisotropic metamaterial cladding design for crosstalk suppression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8fed16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "postprocess(sim_data_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b70b2c2",
   "metadata": {},
   "source": [
    "## Final Remark "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053647fa",
   "metadata": {},
   "source": [
    "The proposed design in this example achieves exceptional coupling only for the TE mode. A different anisotropic metamaterial-based design achieving zero crosstalk for the TM mode is proposed by the same research group. For detail, please refer to `Kabir, M.F., Mia, M.B., Ahmed, I. et al. Anisotropic leaky-like perturbation with subwavelength gratings enables zero crosstalk. Light Sci Appl 12, 135 (2023)` [DOI:10.1038/s41377-023-01184-5](https://doi.org/10.1038/s41377-023-01184-5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bceddcc7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook investigates the utilization of an anisotropic metamaterial to reduce waveguide crosstalk by leveraging exceptional coupling.",
  "feature_image": "./img/exceptional_coupling_te.png",
  "features": [
   "GDS component"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "crosstalk, waveguide, photonic integrated circuit, PIC, Tidy3D, FDTD, anisotropic metamaterial, SWG",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.12"
  },
  "title": "Exceptional Coupling for Crosstalk Reduction | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "05cf63f584e4441598505ea1d774bfea": {
      "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
      }
     },
     "3a291a03a9574e2ea4393492faa57ad9": {
      "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
      }
     },
     "4d66899f5208490f964ff4b313f99155": {
      "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_05cf63f584e4441598505ea1d774bfea",
       "msg_id": "",
       "outputs": [
        {
         "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'eskid'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'eskid'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "6046b4afc8124dbd8e7cfeb6f894e377": {
      "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
      }
     },
     "61751b0e7b9f460f876275f983451a29": {
      "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_3a291a03a9574e2ea4393492faa57ad9",
       "msg_id": "",
       "outputs": [
        {
         "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">13.0/13.0 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\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[32m13.0/13.0 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "61af25350ac44827b50c80491e122b4b": {
      "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
      }
     },
     "62c6587fe91745f898aa632046754720": {
      "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
      }
     },
     "633335c389864e729a81619f7d0a0774": {
      "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_a6983ccdc9c042c2bc9e41663570b13a",
       "msg_id": "",
       "outputs": [
        {
         "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Starting 'ref'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mStarting 'ref'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "76c4b5aa7f874f8f9b450447f5c8c579": {
      "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_caa7400ecf2f4bdb91d109c3de66d90a",
       "msg_id": "d4fb1b07-1faf6860def4b3472db5084a_17390_19",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 2.81e-01) <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━━━━━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\"> 40%</span> <span style=\"color: #008080; text-decoration-color: #008080\">-:--:--</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 2.81e-01) \u001b[38;2;249;38;114m━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 40%\u001b[0m \u001b[36m-:--:--\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "8fb2ac1e33624edea76c2e0ec4dcda75": {
      "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_daa4c5067cb5456e9682f34a13666e05",
       "msg_id": "",
       "outputs": [
        {
         "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: #008000; text-decoration-color: #008000; font-weight: bold\">↓</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">monitor_data.hdf5</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">31.9/31.9 MB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">11.8 MB/s</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;32m↓\u001b[0m \u001b[1;34mmonitor_data.hdf5\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m31.9/31.9 MB\u001b[0m • \u001b[31m11.8 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "a6983ccdc9c042c2bc9e41663570b13a": {
      "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
      }
     },
     "aac935974af34257b618503c33250307": {
      "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_62c6587fe91745f898aa632046754720",
       "msg_id": "",
       "outputs": [
        {
         "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: #008000; text-decoration-color: #008000\">🏃 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'ref'...</span>\n</pre>\n",
          "text/plain": "\u001b[32m🏃 \u001b[0m \u001b[1;32mFinishing 'ref'...\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "caa7400ecf2f4bdb91d109c3de66d90a": {
      "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
      }
     },
     "daa4c5067cb5456e9682f34a13666e05": {
      "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
      }
     },
     "e33d37bae3584805ba10703ecd8cbbff": {
      "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_61af25350ac44827b50c80491e122b4b",
       "msg_id": "",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">solver progress (field decay = 4.29e-05) <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\n",
          "text/plain": "solver progress (field decay = 4.29e-05) \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
      }
     },
     "f39d93696a474b0da48617425fe0df34": {
      "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_6046b4afc8124dbd8e7cfeb6f894e377",
       "msg_id": "",
       "outputs": [
        {
         "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: #800000; text-decoration-color: #800000; font-weight: bold\">↑</span> <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">simulation.hdf5.gz</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100.0%</span> • <span style=\"color: #008000; text-decoration-color: #008000\">5.2/5.2 kB</span> • <span style=\"color: #800000; text-decoration-color: #800000\">?</span> • <span style=\"color: #008080; text-decoration-color: #008080\">0:00:00</span>\n</pre>\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[32m5.2/5.2 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}