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   "source": [
    "# Waveguide crossing based on cosine tapers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5b9158",
   "metadata": {},
   "source": [
    "To achieve a high integration density on a photonic chip, efficient routing of light with low loss using compact junctions is necessary. Therefore, waveguide crossings are crucial building blocks in high performance integrated circuits. \n",
    "\n",
    "This example model demonstrates the simulation of a waveguide crossing based on cosine tapers. The convex cosine taper focuses the guided mode at the center of the crossing junction. This ensures the light is efficiently transmitted into the through port instead of scattered into the cross ports. The device achieves an insertion loss of ~0.2 dB and crosstalk ~-30 dB in the O-band (1260 nm -1360 nm). The design is adapted from [Sujith Chandran, et al. \"Beam shaping for ultra-compact waveguide crossings on monolithic silicon photonics platform,\" Opt. Lett. 45, 6230-6233 (2020)](https://opg.optica.org/ol/abstract.cfm?uri=ol-45-22-6230).\n",
    "\n",
    "<img src=\"img/waveguide_crossing.png\">"
   ]
  },
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       "\u001b[2;36m[21:42:50]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on   \u001b]8;id=300549;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=116576;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m         PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m               \u001b[0m\n",
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       "<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: #000080; text-decoration-color: #000080\">INFO    </span> Using client version: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.9</span>.0rc1                                                  <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">121</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1                                                  \u001b]8;id=755083;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=554769;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n"
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   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from scipy.optimize import fsolve\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290a838f",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85200028",
   "metadata": {},
   "source": [
    "Define geometric parameters and materials. In this device, the Si waveguide has a thickness of 161 nm and a width of 350 nm. "
   ]
  },
  {
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   "execution_count": 2,
   "id": "2f5c50ea",
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    "execution": {
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   "source": [
    "h = 0.161  # waveguide thickness\n",
    "w_in = 0.35  # input taper width\n",
    "w_out = 1.1  # output taper width\n",
    "w_m = 0.75  # amplitude of the cos function\n",
    "l_t = 5.3  # taper length\n",
    "inf_eff = (\n",
    "    1000  # effective infinity used to make sure the waveguides extend into the pml\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b92b8e9",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "si = td.Medium(permittivity=3.67**2)\n",
    "sio2 = td.Medium(permittivity=1.45**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "718becc3",
   "metadata": {},
   "source": [
    "The taper width is described by a cosine function $w(x)=w_m cos(ax+b)$. To determine the parameters $a$ and $b$, we solve a system of equations to ensure $w(x)=w_{in}/2$ at the beginning of the taper and $w(x)=w_{out}/2$ at the end of the taper. This can be easily done using fsolve from Scipy.\n",
    "\n",
    "After we obtain $w(x)$, vertices can be generated and the taper can be made using PolySlab. Once the first taper is made, the rest three tapers can be made by manipulating the vertices with the symmetry relation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e2ef818",
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    "execution": {
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   "source": [
    "# numerically solve for the cos function that describes the taper shape\n",
    "def equations(x0):\n",
    "    a, b = x0\n",
    "    return (\n",
    "        w_m * np.cos(a * (-w_out / 2) + b) - w_out / 2,\n",
    "        w_m * np.cos(a * (-w_out / 2 - l_t) + b) - w_in / 2,\n",
    "    )\n",
    "\n",
    "\n",
    "a, b = fsolve(equations, (0.5, 2))\n",
    "\n",
    "x = np.linspace(-w_out / 2 - l_t, -w_out / 2, 30)\n",
    "w = w_m * np.cos(a * x + b)\n",
    "\n",
    "# using the calculated taper shape to construct the taper as a PolySlab\n",
    "vertices = np.zeros((2 * len(x), 2))\n",
    "vertices[:, 0] = np.concatenate((x, np.flipud(x)))\n",
    "vertices[:, 1] = np.concatenate((w, -np.flipud(w)))\n",
    "taper_1 = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-h / 2, h / 2)),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "# creating the other four tapers by manipulating the vertices of the first taper\n",
    "vertices[:, 0] = -vertices[:, 0]\n",
    "taper_2 = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-h / 2, h / 2)),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "vertices[:, [1, 0]] = vertices[:, [0, 1]]\n",
    "taper_3 = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-h / 2, h / 2)),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "vertices[:, 1] = -vertices[:, 1]\n",
    "taper_4 = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-h / 2, h / 2)),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "# creating the center crossing junction using a Box\n",
    "corner = 0\n",
    "center = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-w_out / 2 - corner, -w_out / 2 - corner, -h / 2),\n",
    "        rmax=(w_out / 2 + corner, w_out / 2 + corner, h / 2),\n",
    "    ),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "# creating the input port and through port\n",
    "horizontal_wg = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-inf_eff, -w_in / 2, -h / 2), rmax=(inf_eff, w_in / 2, h / 2)\n",
    "    ),\n",
    "    medium=si,\n",
    ")\n",
    "\n",
    "# creating the cross ports\n",
    "vertical_wg = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-w_in / 2, -inf_eff, -h / 2), rmax=(w_in / 2, inf_eff, h / 2)\n",
    "    ),\n",
    "    medium=si,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074d7cde",
   "metadata": {},
   "source": [
    "Set up simulation domain, source, and monitors. A mode source is used to excite the input waveguide. A field monitor at $z=0$ plane is added to monitor the field propagation. Two flux monitors are added at the through port and cross port to monitor the transmission and crosstalk levels.\n",
    "\n",
    "Before running the simulation, we can use the plot method to ensure the geometry, source, and monitors are set up correctly."
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at z=0.00'}, xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lda0 = 1.31  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # operation frequency\n",
    "ldas = np.linspace(1.26, 1.36, 101)  # wavelength range of interest\n",
    "freqs = td.C_0 / ldas  # frequency range of interest\n",
    "\n",
    "l_wg = 1  # input/output waveguide length\n",
    "Lx = 2 * l_t + w_out + 2 * l_wg\n",
    "Ly = 2 * l_t + w_out + 2 * l_wg\n",
    "Lz = 1.5 * lda0\n",
    "sim_size = (Lx, Ly, Lz)\n",
    "\n",
    "# define a mode source for excitation using the lowest order mode\n",
    "mode_source = td.ModeSource(\n",
    "    center=(-Lx / 2 + l_wg / 2, 0, 0),\n",
    "    size=(0, 4 * w_in, 4 * h),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    "    direction=\"+\",\n",
    "    mode_spec=td.ModeSpec(num_modes=1, target_neff=3.455, filter_pol=\"te\"),\n",
    "    mode_index=0,\n",
    ")\n",
    "\n",
    "# add a field monitor at z=0 for field visualization\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")\n",
    "\n",
    "# define a flux monitor to detect transmission to the through port\n",
    "flux_monitor_through = td.FluxMonitor(\n",
    "    center=(Lx / 2 - l_wg / 2, 0, 0),\n",
    "    size=(0, 4 * w_in, 4 * h),\n",
    "    freqs=freqs,\n",
    "    name=\"flux_through\",\n",
    ")\n",
    "\n",
    "# define a flux monitor to detect transmission to the cross port\n",
    "flux_monitor_cross = td.FluxMonitor(\n",
    "    center=(0, Ly / 2 - l_wg / 2, 0),\n",
    "    size=(4 * w_in, 0, 4 * h),\n",
    "    freqs=freqs,\n",
    "    name=\"flux_cross\",\n",
    ")\n",
    "\n",
    "sim = td.Simulation(\n",
    "    center=(0, 0, 0),\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n",
    "    structures=[taper_1, taper_2, taper_3, taper_4, center, horizontal_wg, vertical_wg],\n",
    "    sources=[mode_source],\n",
    "    monitors=[field_monitor, flux_monitor_through, flux_monitor_cross],\n",
    "    run_time=1e-12,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    medium=sio2,\n",
    ")\n",
    "\n",
    "sim.plot(z=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e0cca4",
   "metadata": {},
   "source": [
    "Once the simulation set up is verified, submit job to the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0d7dff4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T03:42:51.290745Z",
     "iopub.status.busy": "2023-02-03T03:42:51.290555Z",
     "iopub.status.idle": "2023-02-03T03:44:13.494218Z",
     "shell.execute_reply": "2023-02-03T03:44:13.493707Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\u001b[2;36m[21:42:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'waveguide_crossing'\u001b[0m with task_id                                    \u001b]8;id=281729;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=554107;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\u001b[0m\u001b]8;;\u001b\\\n",
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "\u001b[?25l"
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       "</pre>\n"
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       "\n",
       "\u001b[?25h"
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       "</pre>\n"
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       "\u001b[2;36m[21:42:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Maximum FlexUnit cost: \u001b[1;36m0.488\u001b[0m                                                      \u001b]8;id=828988;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=591686;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#253\u001b\\\u001b[2m253\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
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       "\u001b[2;36m[21:43:03]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m starting up solver                                                                \u001b]8;id=412101;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=38047;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#278\u001b\\\u001b[2m278\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m[21:43:14]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m running solver                                                                    \u001b]8;id=933450;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=336774;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#284\u001b\\\u001b[2m284\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m[21:43:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m early shutoff detected, exiting.                                                  \u001b]8;id=143296;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=858791;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#295\u001b\\\u001b[2m295\u001b[0m\u001b]8;;\u001b\\\n"
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       "</pre>\n"
      ],
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       "\u001b[?25h\r",
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       "</pre>\n"
      ],
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       "\u001b[2;36m[21:44:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Billed FlexUnit cost: \u001b[1;36m0.274\u001b[0m                                                       \u001b]8;id=30048;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=586302;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#311\u001b\\\u001b[2m311\u001b[0m\u001b]8;;\u001b\\\n"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": [
       "\u001b[?25l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "model_id": "780a16fd0e5943308c9d8ec1609a0299",
       "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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n",
       "\u001b[?25h"
      ]
     },
     "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\">[21:44:13] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> loading SimulationData from data/simulation_data.hdf5                             <a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">415</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[21:44:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/simulation_data.hdf5                             \u001b]8;id=357104;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=834267;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job = web.Job(simulation=sim, task_name=\"waveguide_crossing\")\n",
    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998f20e2",
   "metadata": {},
   "source": [
    "## Result Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2772b63f",
   "metadata": {},
   "source": [
    "After the simulation is complete, first plot the field intensity distribution. \n",
    "\n",
    "From the figure below, a good transmission to the through port is observed. In the crossing junction, a strong field focus is formed due to the focusing property of the cosine taper. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2514be3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T03:44:14.747341Z",
     "iopub.status.busy": "2023-02-03T03:44:14.747221Z",
     "iopub.status.idle": "2023-02-03T03:44:16.651217Z",
     "shell.execute_reply": "2023-02-03T03:44:16.650785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'cross section at z=0.00'}, xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data.plot_field(\"field\", \"int\", f=freq0, vmin=0, vmax=3000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0ec431",
   "metadata": {},
   "source": [
    "Finally, to quantify the designed waveguide crossing's performance, plot insertion loss and crosstalk level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6959a333",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-03T03:44:16.653663Z",
     "iopub.status.busy": "2023-02-03T03:44:16.653386Z",
     "iopub.status.idle": "2023-02-03T03:44:16.957749Z",
     "shell.execute_reply": "2023-02-03T03:44:16.957352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-30.0, -25.0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 5))\n",
    "\n",
    "T_through = sim_data[\"flux_through\"].flux\n",
    "T_cross = sim_data[\"flux_cross\"].flux\n",
    "\n",
    "ax1.plot(ldas, -10 * np.log10(T_through))\n",
    "ax1.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
    "ax1.set_ylabel(\"Insertion loss (dB)\")\n",
    "ax1.set_ylim((0, 0.3))\n",
    "\n",
    "ax2.plot(ldas, 10 * np.log10(T_cross))\n",
    "ax2.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
    "ax2.set_ylabel(\"Crosstalk (dB)\")\n",
    "ax2.set_ylim((-30, -25))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60f5fdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "3d3840863fe84f7a982e94b804ceee31": {
      "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_8ca49955b2cc42c9ae29257fc8bf7d99",
       "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\">% done (field decay = 2.06e-06) <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\"> 56%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:32</span>\n% done (field decay = 2.06e-06) <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\"> 56%</span> <span style=\"color: #008080; text-decoration-color: #008080\">0:00:32</span></pre>\n",
          "text/plain": "\r\u001b[2K% done (field decay = 2.06e-06) \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 56%\u001b[0m \u001b[36m0:00:32\u001b[0m\n% done (field decay = 2.06e-06) \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 56%\u001b[0m \u001b[36m0:00:32\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "48e985cb49a546fd8221dfc83f2be976": {
      "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_cef4e216897143fbab3644eb4c2fd65a",
       "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 'waveguide_crossing'...</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">🚶 </span> <span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Finishing 'waveguide_crossing'...</span></pre>\n",
          "text/plain": "\r\u001b[2K\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'waveguide_crossing'...\u001b[0m\n\u001b[32m🚶 \u001b[0m \u001b[1;32mFinishing 'waveguide_crossing'...\u001b[0m"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ],
       "tabbable": null,
       "tooltip": null
      }
     },
     "74e51147bb77496dbe504bea978d72e5": {
      "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",
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