{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "377be9a8",
   "metadata": {},
   "source": [
    "# Waveguide to ring coupling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "557f4505",
   "metadata": {},
   "source": [
    "Optical ring resonators are key components in the field of integrated photonics. The unique capability of ring resonators to selectively interact with specific wavelengths of light makes them extremely versatile. They can be utilized in a broad array of applications, including optical filtering, modulating, switching, and sensing. However, simulating a ring resonator can be computationally expensive due to the high-Q resonances. Alternatively, we can investigate the coupling between a straight waveguide to a ring only by simulating the coupling region. The coupling coefficients can be extracted from this much simpler simulation. \n",
    "\n",
    "Noticeably, in a waveguide-to-ring simulation, one of the simulation domain boundaries intersects the ring structure. Non-translational invariant structures inside PML are known to cause instabilities in a FDTD simulation so such a simulation is likely to **diverge**. In this notebook, we will try to use the PML boundary first to see if the simulation diverges. When it does, we will apply an effective remedy under this situation: **replacing the PML with the adiabatic absorber boundary**. The absorber functions similarly to PML such that it absorbs the outgoing radiation to mimic the infinite space. However, the absorber has a slightly higher reflection and requires a bit more computation than PML but it is numerically much more stable.\n",
    "\n",
    "FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it. \n",
    "\n",
    "<img src=\"img/waveguide_to_ring.png\" width=\"600\" alt=\"Schematic of the waveguide to ring coupling\">\n",
    "\n",
    "For more integrated photonic examples such as the [8-Channel mode and polarization de-multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/8ChannelDemultiplexer/), the [broadband bi-level taper polarization rotator-splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/BilevelPSR/), and the [broadband directional coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/BroadbandDirectionalCoupler/), please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/).\n",
    "\n",
    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "878ff685",
   "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": "4316bdf7",
   "metadata": {},
   "source": [
    "## Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f64a91",
   "metadata": {},
   "source": [
    "Define simulation wavelength range to be 1.5 $\\mu m$ to 1.6 $\\mu m$.\n",
    "."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51e608fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "ldas = np.linspace(1.5, 1.6, 101)  # wavelength range of interest\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "freqs = td.C_0 / ldas  # frequency range of interest\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # frequency width of the source"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2cce2e",
   "metadata": {},
   "source": [
    "The ring and waveguide are made of silicon. The top and bottom claddings are made of silicon oxide. Here we use the materials from Tidy3D's [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html) directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c889b560",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define silicon and silicon dioxide media from the material library\n",
    "si = td.material_library[\"cSi\"][\"Li1993_293K\"]\n",
    "sio2 = td.material_library[\"SiO2\"][\"Horiba\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1733665d",
   "metadata": {},
   "source": [
    "Define the geometric parameters. The waveguide is 500 nm wide and 220 nm thick. The ring has a radius of 5 $\\mu m$ and the gap size is 50 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b9ec897",
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 0.5  # width of the waveguide\n",
    "h_si = 0.22  # thickness of the silicon layer\n",
    "gap = 0.05  # gap size between the waveguides and the ring\n",
    "r = 5  # radius of the ring\n",
    "inf_eff = 1e2  # effective infinity\n",
    "\n",
    "# simulation domain size\n",
    "Lx = 2 * r + 2 * lda0\n",
    "Ly = r / 2 + gap + 2 * w + lda0\n",
    "Lz = 9 * h_si"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8d4a3d",
   "metadata": {},
   "source": [
    "We only need to define two structures: a straight waveguide and a ring. Both are commonly used PIC components introduced in the [demonstration notebook](../notebooks/PICComponents.html). We can simply copy the associated functions here and them use to define the structures quickly. Namely, we copy the `straight_waveguide` function and the `ring_resonator` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63cbe626",
   "metadata": {},
   "outputs": [],
   "source": [
    "def straight_waveguide(x0, y0, z0, x1, y1, wg_width, wg_thickness, medium, sidewall_angle=0):\n",
    "    \"\"\"\n",
    "    This function defines a straight strip waveguide and returns the tidy3d structure of it.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x0: x coordinate of the waveguide starting position (um)\n",
    "    y0: y coordinate of the waveguide starting position (um)\n",
    "    z0: z coordinate of the waveguide starting position (um)\n",
    "    x1: x coordinate of the waveguide end position (um)\n",
    "    y1: y coordinate of the waveguide end position (um)\n",
    "    wg_width: width of the waveguide (um)\n",
    "    wg_thickness: thickness of the waveguide (um)\n",
    "    medium: medium of the waveguide\n",
    "    sidewall_angle: side wall angle of the waveguide (rad)\n",
    "    \"\"\"\n",
    "\n",
    "    cell = gdstk.Cell(\"waveguide\")  # define a gds cell\n",
    "\n",
    "    path = gdstk.RobustPath((x0, y0), wg_width, layer=1, datatype=0)  # define a path\n",
    "    path.segment((x1, y1))\n",
    "\n",
    "    cell.add(path)  # add path to the cell\n",
    "\n",
    "    # define geometry from the gds cell\n",
    "    wg_geo = td.PolySlab.from_gds(\n",
    "        cell,\n",
    "        gds_layer=1,\n",
    "        axis=2,\n",
    "        slab_bounds=(z0 - wg_thickness / 2, z0 + wg_thickness / 2),\n",
    "        sidewall_angle=sidewall_angle,\n",
    "    )\n",
    "\n",
    "    # define tidy3d structure of the bend\n",
    "    wg = td.Structure(geometry=wg_geo[0], medium=medium)\n",
    "\n",
    "    return wg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "844820b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ring_resonator(\n",
    "    x0,\n",
    "    y0,\n",
    "    z0,\n",
    "    R,\n",
    "    wg_width,\n",
    "    wg_thickness,\n",
    "    medium,\n",
    "    sidewall_angle=0,\n",
    "):\n",
    "    \"\"\"\n",
    "    This function defines a ring and returns the tidy3d structure of it.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x0: x coordinate of center of the ring (um)\n",
    "    y0: y coordinate of center of the ring (um)\n",
    "    z0: z coordinate of center of the ring (um)\n",
    "    R: radius of the ring (um)\n",
    "    wg_width: width of the waveguide (um)\n",
    "    wg_thickness: thickness of the waveguide (um)\n",
    "    medium: medium of the waveguide\n",
    "    sidewall_angle: side wall angle of the waveguide (rad)\n",
    "    \"\"\"\n",
    "\n",
    "    cell = gdstk.Cell(\"top\")  # define a gds cell\n",
    "\n",
    "    # define a path\n",
    "    path_top = gdstk.RobustPath(\n",
    "        (x0 + R, y0), wg_width - wg_thickness * np.tan(np.abs(sidewall_angle)), layer=1, datatype=0\n",
    "    )\n",
    "    path_top.arc(R, 0, np.pi)  # make the top half of the ring\n",
    "    cell.add(path_top)  # add path to the cell\n",
    "\n",
    "    # the reference plane depends on the sign of the sidewall_angle\n",
    "    if sidewall_angle >= 0:\n",
    "        reference_plane = \"top\"\n",
    "    else:\n",
    "        reference_plane = \"bottom\"\n",
    "\n",
    "    # define top half ring geometry\n",
    "    ring_top_geo = td.PolySlab.from_gds(\n",
    "        cell,\n",
    "        gds_layer=1,\n",
    "        axis=2,\n",
    "        slab_bounds=(z0 - wg_thickness / 2, z0 + wg_thickness / 2),\n",
    "        sidewall_angle=sidewall_angle,\n",
    "        reference_plane=reference_plane,\n",
    "    )\n",
    "\n",
    "    # similarly for the bottom half of the ring\n",
    "    cell = gdstk.Cell(\"bottom\")\n",
    "    path_bottom = gdstk.RobustPath(\n",
    "        (x0 + R, y0), wg_width - wg_thickness * np.tan(np.abs(sidewall_angle)), layer=1, datatype=0\n",
    "    )\n",
    "\n",
    "    path_bottom.arc(R, 0, -np.pi)\n",
    "    cell.add(path_bottom)\n",
    "\n",
    "    ring_bottom_geo = td.PolySlab.from_gds(\n",
    "        cell,\n",
    "        gds_layer=1,\n",
    "        axis=2,\n",
    "        slab_bounds=(z0 - wg_thickness / 2, z0 + wg_thickness / 2),\n",
    "        sidewall_angle=sidewall_angle,\n",
    "        reference_plane=reference_plane,\n",
    "    )\n",
    "\n",
    "    # define ring structure\n",
    "    ring = td.Structure(\n",
    "        geometry=td.GeometryGroup(geometries=ring_bottom_geo + ring_top_geo), medium=medium\n",
    "    )\n",
    "\n",
    "    return ring"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebad698",
   "metadata": {},
   "source": [
    "Use the above functions to define the [Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af37300c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define straight waveguide\n",
    "waveguide = straight_waveguide(\n",
    "    x0=-inf_eff,\n",
    "    y0=0,\n",
    "    z0=0,\n",
    "    x1=inf_eff,\n",
    "    y1=0,\n",
    "    wg_width=w,\n",
    "    wg_thickness=h_si,\n",
    "    medium=si,\n",
    "    sidewall_angle=0,\n",
    ")\n",
    "\n",
    "# define ring\n",
    "ring = ring_resonator(\n",
    "    x0=0,\n",
    "    y0=w + gap + r,\n",
    "    z0=0,\n",
    "    R=r,\n",
    "    wg_width=w,\n",
    "    wg_thickness=h_si,\n",
    "    medium=si,\n",
    "    sidewall_angle=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3019d41d",
   "metadata": {},
   "source": [
    "We will use a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) to excite the straight waveguide using the fundamental TE mode. A [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) is placed at the through to measure the transmission. Another [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) is placed at the ring to measure the coupling to the ring. For the monitor at the ring, we need to properly set up `angle_theta`, `angle_theta`, `bend_radius`, and `bend_axis` in the [ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html) as demonstrated in the [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/ModesBentAngled/). Finally, we add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) to help visualize the field distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68e1f253",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_si = 3.47\n",
    "# mode spec for the source\n",
    "mode_spec_source = td.ModeSpec(num_modes=1, target_neff=n_si)\n",
    "# mode spec for the through port\n",
    "mode_spec_through = mode_spec_source\n",
    "# angle of the mode at the ring\n",
    "theta = np.pi / 4\n",
    "# mode spec for the drop port at the ring\n",
    "mode_spec_drop = td.ModeSpec(\n",
    "    num_modes=1, target_neff=n_si, angle_theta=theta, bend_radius=r, bend_axis=1\n",
    ")\n",
    "\n",
    "# add a mode source as excitation\n",
    "mode_source = td.ModeSource(\n",
    "    center=(-r - lda0 / 4, 0, 0),\n",
    "    size=(0, 6 * w, 6 * h_si),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    mode_spec=mode_spec_source,\n",
    "    mode_index=0,\n",
    ")\n",
    "\n",
    "# add a mode monitor to measure transmission at the through port\n",
    "mode_monitor_through = td.ModeMonitor(\n",
    "    center=(r + lda0 / 4, 0, 0),\n",
    "    size=mode_source.size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec_through,\n",
    "    name=\"through\",\n",
    ")\n",
    "\n",
    "# add a mode monitor to measure transmission at the drop port\n",
    "mode_monitor_drop = td.ModeMonitor(\n",
    "    center=(np.sin(theta) * r, w + gap + r - np.cos(theta) * r, 0),\n",
    "    size=(6 * w, 0, 6 * h_si),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec_drop,\n",
    "    name=\"drop\",\n",
    ")\n",
    "\n",
    "# add a field monitor to visualize the field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89caf8a8",
   "metadata": {},
   "source": [
    "## Using PML Boundary "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c7e177",
   "metadata": {},
   "source": [
    "Finally, we define the Simulation. Intuitively, we would use the [PML](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PML.html) boundary on all sides similar to what we did in most other PIC component simulations. Let's try it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0095a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_time = 2e-12  # simulation run time\n",
    "\n",
    "# construct simulation\n",
    "sim_pml = td.Simulation(\n",
    "    center=(0, Ly / 4, 0),\n",
    "    size=(Lx, Ly, Lz),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=25, wavelength=lda0),\n",
    "    structures=[waveguide, ring],\n",
    "    sources=[mode_source],\n",
    "    monitors=[mode_monitor_through, mode_monitor_drop, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    medium=sio2,\n",
    "    symmetry=(0, 0, 1),\n",
    ")\n",
    "\n",
    "# plot simulation\n",
    "sim_pml.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac85d93e",
   "metadata": {},
   "source": [
    "Submit the simulation to the server to run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0eb7ca6",
   "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\">12:31:37 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'waveguide_to_ring'</span> with task_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-002f22b4-6230-4b1a-b25b-426f2697ad53'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:37 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'waveguide_to_ring'\u001b[0m with task_id                     \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-002f22b4-6230-4b1a-b25b-426f2697ad53'\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-002f22b4-6230-4b1a-b25b-426f2697ad53\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-62</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">30-4b1a-b25b-426f2697ad53'</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=249319;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=712624;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=249319;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=937290;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=249319;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[32m-002f22b4-62\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=249319;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[32m30-4b1a-b25b-426f2697ad53'\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=750019;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": "50cc36d2d5484cf9aa5beb0d7be7909f",
       "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\">12:31:40 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.469</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;36m12:31:40 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.469\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\">12:31:41 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:41 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0d4143e87ae455cb5265513107eea28",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:31:56 CEST </span>status = preprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:31:56 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \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\"></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\">12:32:00 CEST </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:00 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \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\">12:32:01 CEST </span>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:01 CEST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "584b054b80e641159a3b44982c27ebe6",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:32:18 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">52</span>%, exiting.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:18 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m52\u001b[0m%, exiting.                           \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\"></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\">              </span>status = postprocess                                              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0238037ebf564554b12e1bb23ba95b2f",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:32:21 CEST </span>status = diverged                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:21 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = diverged                                                 \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\"></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\">12:32:23 CEST </span>View simulation result at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-62</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">30-4b1a-b25b-426f2697ad53'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:23 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=235425;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=105137;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=235425;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=665813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=235425;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[4;34m-002f22b4-62\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=235425;https://tidy3d.simulation.cloud/workbench?taskId=fdve-002f22b4-6230-4b1a-b25b-426f2697ad53\u001b\\\u001b[4;34m30-4b1a-b25b-426f2697ad53'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01407e9302944d8dbc34ef06a02627da",
       "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\">12:32:36 CEST </span>loading simulation from data/simulation_data.hdf5                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:36 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation_data.hdf5                 \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The simulation has diverged! For more information, check </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.          </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: The simulation has diverged! For more information, check \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.          \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(\n",
    "    simulation=sim_pml, task_name=\"waveguide_to_ring\", path=\"data/simulation_data.hdf5\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888f5be1",
   "metadata": {},
   "source": [
    "Now we see that the status of the job is shown to be **diverged**. As discussed in the introduction, it should not come as a surprise since the ring section in the PML layer is not perpendicular to the boundary and translational invariant as shown in the zoomed-in plot below. We see that we are also given a warning about the divergence and prompted to check the log for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9af23884-fff1-4900-bc79-78b7f46e9518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:31:55] USER: Simulation domain Nx, Ny, Nz: [759, 280, 82]                   \n",
      "           USER: Applied symmetries: (0, 0, 1)                                  \n",
      "           USER: Number of computational grid points: 9.2279e+06.               \n",
      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
      "           timestep_reduction=0.3, edge_singularity_correction=False),          \n",
      "           pmc=Staircasing(attrs={}, type='Staircasing'),                       \n",
      "           lossy_metal=SurfaceImpedance(attrs={}, type='SurfaceImpedance',      \n",
      "           timestep_reduction=0.0, edge_singularity_correction=False),          \n",
      "           type='SubpixelSpec')                                                 \n",
      "           USER: Number of time steps: 6.2950e+04                               \n",
      "           USER: Automatic shutoff factor: 1.00e-05                             \n",
      "           USER: Time step (s): 3.1772e-17                                      \n",
      "           USER:                                                                \n",
      "                                                                                \n",
      "           USER: Mode solver at f=1.9341448903e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:56] USER: Compute source modes time (s):     1.2065                      \n",
      "[10:31:57] USER: Rest of setup time (s):            0.4643                      \n",
      "[10:31:59] USER: Mode solver at f=1.9827543519e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9986163867e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9933009176e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9723188026e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9619925262e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9775228100e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9671421129e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:31:59] USER: Mode solver at f=1.9880136472e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9972848634e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9919764651e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9866962094e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9710220776e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9658521836e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9814438731e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9762192353e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:00] USER: Mode solver at f=1.9607093394e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9959551132e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9906537716e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9801351255e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9853805166e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9697270565e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9749173781e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9594278301e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9645639450e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9946271324e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9893328334e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9788281056e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9840665652e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9684337360e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9581479948e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9736172350e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9632773936e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9568698303e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9517738151e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9467042727e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9416609974e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9366437855e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9316524356e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9266867481e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:01] USER: Mode solver at f=1.9217465256e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9454409994e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9555933333e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9505039558e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9404042589e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9353935313e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9254493128e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9304086156e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9205154260e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9441793645e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9543185007e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9492357477e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9341448903e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9391491462e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9242134660e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9291663964e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9192859027e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9429193649e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9530453290e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9479691878e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9328978594e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9378956561e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9229792046e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9279257749e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9180579527e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9168315729e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9119416964e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:02] USER: Mode solver at f=1.9070767048e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9022364086e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8974206203e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8926291540e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8878618262e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8831184548e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9107231230e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9156067604e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9058643229e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9010301712e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8962204807e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8914350662e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8866737445e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8819363340e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9095061019e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9143835121e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.9046534816e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8950218584e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8998254626e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8902424842e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8854871572e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.8807556964e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.9082906302e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.9131618251e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.9034441778e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.8938247505e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.8986222799e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.8890514052e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:03] USER: Mode solver at f=1.8843020616e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8795765392e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8783988596e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8737028625e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8772226550e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:04] USER: Mode solver at f=1.8760479224e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "           USER: Mode solver at f=1.8748746592e+14 with plane size (135, 24),   \n",
      "           direction: +                                                         \n",
      "[10:32:15] USER: Solver time (s):                   15.6420                     \n",
      "           USER: Time-stepping speed (cells/s):     2.14e+10                    \n",
      "           WARNING: The simulation has diverged!                                \n",
      "           WARNING: Some structures were found to be spatially varying inside   \n",
      "           the PML. Ensure that structures are translationally invariant in the \n",
      "           PML regions in the direction normal to the PML interface.            \n",
      "           Alternatively, switching the PML to Absorber boundary should fix the \n",
      "           divergence.                                                          \n",
      "           WARNING: A dispersive medium inside the PML regions was detected,    \n",
      "           which may be causing the divergence. Consider making the medium      \n",
      "           non-dispersive, or fitting a different dispersive model for that     \n",
      "           medium. Alternatively, switching the PML to Absorber boundary should \n",
      "           fix the divergence.                                                  \n",
      "           USER: Post-processing time (s):          0.5682                      \n",
      "\n",
      " ====== SOLVER LOG ====== \n",
      "\n",
      "Processing grid and structures...\n",
      "Building FDTD update coefficients...\n",
      "Potential divergence: dispersive medium into PML.\n",
      "Potential divergence: structures are not translationally invariant inside PML.\n",
      "Solver setup time (s):             0.7388\n",
      "\n",
      "Running solver for 62950 time steps...\n",
      "- Time step   2517 / time 8.00e-14s (  4 % done), field decay: 1.00e+00\n",
      "- Time step   4010 / time 1.27e-13s (  6 % done), field decay: 1.00e+00\n",
      "- Time step   5035 / time 1.60e-13s (  8 % done), field decay: 1.00e+00\n",
      "- Time step   7553 / time 2.40e-13s ( 12 % done), field decay: 1.00e+00\n",
      "- Time step  10071 / time 3.20e-13s ( 16 % done), field decay: 6.82e-02\n",
      "- Time step  12589 / time 4.00e-13s ( 20 % done), field decay: 2.88e-04\n",
      "- Time step  15107 / time 4.80e-13s ( 24 % done), field decay: 1.00e+00\n",
      "- Time step  17625 / time 5.60e-13s ( 28 % done), field decay: 1.00e+00\n",
      "- Time step  20143 / time 6.40e-13s ( 32 % done), field decay: 1.00e+00\n",
      "- Time step  22661 / time 7.20e-13s ( 36 % done), field decay: 1.00e+00\n",
      "- Time step  25179 / time 8.00e-13s ( 40 % done), field decay: 1.00e+00\n",
      "- Time step  27697 / time 8.80e-13s ( 44 % done), field decay: 1.00e+00\n",
      "- Time step  30215 / time 9.60e-13s ( 48 % done), field decay: 1.00e+00\n",
      "- Time step  32733 / time 1.04e-12s ( 52 % done), field decay: 1.00e+00\n",
      "Field diverged at time step  33830 ( 53 % done), exiting solver.\n",
      "Time-stepping time (s):            14.5617\n",
      "Data write time (s):               0.3346\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sim_data.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf3803e-059c-4c5a-93ba-7cd1142e6fa4",
   "metadata": {},
   "source": [
    "The log indeed tells us that there are two potential causes of divergence that were found in this simulation. One is the presence of dispersive materials in the PML, while the other is the fact that the ring is not translationally invariant. While both of those things can cause divergence, our in-built silicon material model has been fit to perform well for straight waveguides going into the PML, at telecommunication wavelengths. Thus, the more likely cause of the divergence is the fact that the ring is not translationally invariant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd12c0bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zoom-in plot around the pml\n",
    "ax = sim_pml.plot(z=0)\n",
    "ax.set_xlim(4, 5.5)\n",
    "ax.set_ylim(3.5, 4.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e04569d-21bc-4f51-ab8b-14f198c14d78",
   "metadata": {},
   "source": [
    "This can be further confirmed by plotting the electric field distribution. From the plot, we can see where the field built up and caused the simulation to diverge. As expected, the field built up around the ring to PML area. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e54aebe9-ae75-430d-a8f9-f2b62d5a1f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145a32ad",
   "metadata": {},
   "source": [
    "## Using Absorber Boundary "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025f42f2",
   "metadata": {},
   "source": [
    "To resolve the diverge, we can switch to [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html). This can be done by copying the previous simulation and only updating the `boundary_spec`. Since the ring is only intersecting the boundary in the y direction, we only need to apply [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html) to the positive $y$ boundary. \n",
    "\n",
    "In addition, we increased the number of layers in the [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html) to `60` to ensure sufficient absorption and minimal reflection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ddf31c0c",
   "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\">12:32:39 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'waveguide_to_ring'</span> with task_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4ea811df-68a7-4b29-b6be-2b070844b263'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:39 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'waveguide_to_ring'\u001b[0m with task_id                     \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-4ea811df-68a7-4b29-b6be-2b070844b263'\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-4ea811df-68a7-4b29-b6be-2b070844b263\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a7-4b29-b6be-2b070844b263'</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=87006;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=661015;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=87006;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=694099;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=87006;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[32m-4ea811df-68\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=87006;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[32ma7-4b29-b6be-2b070844b263'\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=331431;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": "8583a5159aa147eeb79cd4b3b1cc6bf8",
       "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\">12:32:42 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.520</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;36m12:32:42 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.520\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\">12:32:43 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:43 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "930ac20995124596a2f485c28c6efb05",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:32:55 CEST </span>status = preprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:32:55 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \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\"></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\">12:33:00 CEST </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:00 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \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>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c41cb9bdb07a4380a6d94adab66ba32c",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:33:36 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>%, exiting.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:36 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m20\u001b[0m%, exiting.                           \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\"></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\">              </span>status = postprocess                                              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c922613bcb74def95859d5c69fadf7f",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:33:41 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:41 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \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\"></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\">12:33:43 CEST </span>View simulation result at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">a7-4b29-b6be-2b070844b263'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:43 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=622762;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=798379;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622762;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=484185;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=622762;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[4;34m-4ea811df-68\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=622762;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4ea811df-68a7-4b29-b6be-2b070844b263\u001b\\\u001b[4;34ma7-4b29-b6be-2b070844b263'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4942798af8cb472d866f5358eb3d13df",
       "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\">12:33:47 CEST </span>loading simulation from data/simulation_data.hdf5                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:33:47 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation_data.hdf5                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# copy simulation and update boundary condition\n",
    "sim_absorber = sim_pml.copy(\n",
    "    update={\n",
    "        \"boundary_spec\": td.BoundarySpec(\n",
    "            x=td.Boundary.pml(),\n",
    "            y=td.Boundary(plus=td.Absorber(num_layers=60), minus=td.PML()),\n",
    "            z=td.Boundary.pml(),\n",
    "        )\n",
    "    }\n",
    ")\n",
    "\n",
    "# run simulation\n",
    "sim_data = web.run(\n",
    "    simulation=sim_absorber, task_name=\"waveguide_to_ring\", path=\"data/simulation_data.hdf5\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b037372f",
   "metadata": {},
   "source": [
    "After switching to [Absorber](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html), the simulation doesn't run into the divergence issue anymore and thus we can plot the transmission spectra to the through and drop ports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e40af272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract mode amplitude in the through port\n",
    "t = sim_data[\"through\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "# extract mode amplitude in the drop port\n",
    "k = sim_data[\"drop\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "\n",
    "# plot transmission\n",
    "plt.plot(ldas, np.abs(t) ** 2, label=\"Through port\")\n",
    "plt.plot(ldas, np.abs(k) ** 2, label=\"Drop port\")\n",
    "plt.legend()\n",
    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Transmission\")\n",
    "plt.xlim(1.5, 1.6)\n",
    "plt.ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20c7b7a-08ec-4576-a230-ca3e68e53c1a",
   "metadata": {},
   "source": [
    "Again we can visualize the field distribution. This time, we see the correct result where the energy is partially coupled to the ring and partially stays in the straight waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a5b860c-a5f0-4f03-b033-9cf1f1ca6de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6cade11",
   "metadata": {},
   "source": [
    "## Additional Notes on Absorber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8767d8",
   "metadata": {},
   "source": [
    "The adiabatic absorber is a multilayer system with gradually increasing conductivity. As briefly discussed above, the absorber usually has a larger undesired reflection compared to PML. In practice, this small difference rarely matters, but is important to understand for simulations that require high accuracy. There are two possible sources for the reflection from absorbers. The first, and more common one, is that the ramping up of the conductivity is not sufficiently slow, which can be remedied by increasing the number of absorber layers (40 by default). The second one is that the absorption is not high enough, such that the light reaches the PEC boundary at the end of the Absorber, travels back through it, and is still not fully attenuated before re-entering the simulation region. If this is the case, increasing the maximum conductivity (see the [API reference](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html)) can help. In both cases, changing the order of the scaling of the conductivity (`sigma_order`) can also have an effect, but this is a more advanced setting that we typically do not recommend modifying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fee809d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to model a waveguide to ring coupling and avoid simulation divergence.",
  "feature_image": "./img/waveguide_to_ring.png",
  "features": [],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "waveguide, ring, coupling, absorber, diverge, silicon photonics, PIC, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.12"
  },
  "title": "Modeling the Waveguide to Ring Coupling | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0d1fc80e8e10486e8f29ee66324e9ca5": {
      "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_d2f186a246524b59a288acc921b884c8",
       "msg_id": "",
       "outputs": [
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