{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Two-photon absorption (TPA) and free-carrier absorption (FCA) in a Si\n",
        "\n",
        "waveguide\n",
        "\n",
        "In this notebook, we will reproduce part of the experimental results in\n",
        "`Rong, H., Liu, A., Jones, R. et al. An all-silicon Raman laser. Nature 433, 292–294 (2005)`\n",
        "[DOI: 10.1038/nature03273](https://doi.org/10.1038/nature03273).\n",
        "Specifically, the authors provided experimental results for the\n",
        "input-output power relationship for a 4.8cm long rib waveguide, with the\n",
        "output power following a non-linear dependence on the input power due to\n",
        "two-photon absorption (TPA) and free-carrier absorption (FCA) effects.\n",
        "All of the physical parameters are provided in the paper which makes for\n",
        "a great benchmark to reproduce with the first-principle TPA and FCA\n",
        "support in the Tidy3D solver.\n",
        "\n",
        "<img src=\"img/TPA_FCA.png\" width=\"400\" alt=\"Schematic of the TPA and FCA in a silicon waveguide\">"
      ],
      "id": "542c34d0-df13-4343-91e8-b9d919c688bc"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "from tidy3d import web"
      ],
      "id": "fc35de20"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, define some fundamental parameters as they appear in the paper.\n",
        "It also provides the nonlinear parameters like the TPA coefficient and\n",
        "the FCA optical cross-section. The carrier lifetime $\\tau$ on the other\n",
        "hand is controlled by placing the waveguide in a p-i-n junction and\n",
        "controlling the voltage across the junction, which can help sweep out\n",
        "free carriers faster. We are going to model three different carrier\n",
        "lifetimes, as presented in Fig. 2 of the\n",
        "[paper](https://doi.org/10.1038/nature03273). The paper also reports a\n",
        "linear waveguide loss of 0.35 dB/cm. We will account for it by adding an\n",
        "imaginary part of the permittivity, or equivalently, a finite\n",
        "conductivity to the silicon medium."
      ],
      "id": "70cfac3e-6358-4065-b2e3-c738957c4102"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "wavelength = 1.55  # wavelength of interest\n",
        "freq0 = td.C_0 / wavelength  # corresponding frequency\n",
        "\n",
        "wg_w = 1.5  # rib waveguide width\n",
        "wg_h = 0.7  # rib waveguide height\n",
        "si_h = 0.85  # silicon slab thickness\n",
        "wg_l = 4.8e4  # wavelength length (4.8 cm)\n",
        "\n",
        "si_n0 = 3.47  # refractive index of silicon\n",
        "si_cond = 7.4e-5  # conductivity in S / um of silicon to account for the linear loss\n",
        "tpa_beta = 0.5e-5  # TPA coefficient in um / W\n",
        "fca_sigma = 1.47e-9  # FCA cross-section in um**(-2)"
      ],
      "id": "88c701b3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The TPA + FCA modeling in Tidy3D follows these equations:\n",
        "\n",
        "$$\\begin{align}\n",
        "    P_{NL} &= P_{TPA} + P_{FCA} + P_{FCPD}, \\\\\n",
        "    P_{TPA} &= -\\frac{4}{3}\\frac{c_0^2 \\varepsilon_0^2 n_0^2 \\beta}{2 i \\omega} |E|^2 E, \\\\\n",
        "    P_{FCA} &= -\\frac{c_0 \\varepsilon_0 n_0 \\sigma N_f}{i \\omega} E, \\\\\n",
        "    \\frac{dN_f}{dt} &= \\frac{8}{3}\\frac{c_0^2 \\varepsilon_0^2 n_0^2 \\beta}{8 q_e \\hbar \\omega} |E|^4 - \\frac{N_f}{\\tau}, \\\\\n",
        "    N_e &= N_h = N_f, \\\\\n",
        "    P_{FCPD} &= \\varepsilon_0 2 n_0 \\Delta n (N_f) E, \\\\\n",
        "    \\Delta n (N_f) &= (c_e N_e^{e_e} + c_h N_h^{e_h}),\n",
        "\\end{align}$$\n",
        "\n",
        "where $P$ denote various polarization densities, and $N_f$ is the free\n",
        "carrier concentration. The factors of $\\frac{4}{3}$ and $\\frac{8}{3}$ in\n",
        "the equations are coming from using real fields instead of complex\n",
        "fields in the solver. More details can be found on this\n",
        "[study](https://ieeexplore.ieee.org/document/4299014).\n",
        "\n",
        "We will be looking at continuous-wave operation, in which we evolve the\n",
        "system under a continuous-wave source until a steady state is reached\n",
        "both for the optical oscillations and the free-carrier distribution. In\n",
        "that regime, $\\frac{dN_f}{dt} = 0$, and\n",
        "\n",
        "$$N_f = {\\tau}\\frac{8}{3}\\frac{c_0^2 \\varepsilon_0^2 n_0^2 \\beta}{8 q_e \\hbar \\omega} |E|^4$$\n",
        "\n",
        "This means that the free-carrier polarization density $P_{FCA}$ is\n",
        "proportional to $\\sigma \\tau$, which allows us to make a simplifying\n",
        "adjustment to be able to run the FDTD simulation within a reasonable\n",
        "number of time steps. Namely, the typical $\\tau$ in that system is on\n",
        "the order of nanoseconds, which is much larger than the optical period,\n",
        "and too long for the FDTD simulation. However, we can simultaneously\n",
        "decrease $\\tau$ by a factor and increase $\\sigma$ by the same factor,\n",
        "such that the steady-state concentration remains the same.\n",
        "\n",
        "For this illustration, we also will not be simulating the whole 4.8cm\n",
        "waveguide from the paper. We can scale the TPA coefficient $\\beta$,\n",
        "making the overall nonlinearity stronger, while correspondingly\n",
        "shortening the waveguide. This is a valid modification to the setup\n",
        "since both the two-photon absorption, and the free-carrier concentration\n",
        "$N_f$, and thus the free-carrier absorption, are proportional to\n",
        "$\\beta$."
      ],
      "id": "8e345a0e-3fa4-4a27-bdee-ea5d2bd9b0ae"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# make the tau shorter by increasing sigma\n",
        "tau_scale_factor = 1e-6  # characteristic tau will be femtoseconds instead of nanoseconds\n",
        "\n",
        "# make the waveguide shorter by increasing beta\n",
        "beta_scale_factor = 1e3"
      ],
      "id": "9818a118"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can define a function to generate the simulation. Note that we\n",
        "will not be including the doped silicon regions and the electrodes, as\n",
        "the authors say that they have verified that their introduction does not\n",
        "affect the light propagation in the linear regime."
      ],
      "id": "eff6bbdf-5847-47e6-a52c-03ec298beb9f"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_sim(\n",
        "    tpa: bool = True,\n",
        "    input_power: float = 1.0,\n",
        "    fca_tau: float = 1.2e-9,  # seconds\n",
        "):\n",
        "    # reduce the total waveguide length and correspondingly increase the TPA strength\n",
        "    wg_l_scaled = wg_l / beta_scale_factor\n",
        "\n",
        "    if tpa:\n",
        "        # define TPA specification\n",
        "        # reduce the FCA tau and correspondingly increase the cross-section\n",
        "        tpa_material = td.TwoPhotonAbsorption(\n",
        "            beta=tpa_beta * beta_scale_factor,\n",
        "            tau=fca_tau * tau_scale_factor,\n",
        "            sigma=fca_sigma / tau_scale_factor,\n",
        "        )\n",
        "\n",
        "        # define silicon medium\n",
        "        si = td.Medium(\n",
        "            permittivity=si_n0**2,\n",
        "            conductivity=si_cond,\n",
        "            nonlinear_spec=td.NonlinearSpec(models=[tpa_material], num_iters=5),\n",
        "        )\n",
        "    else:\n",
        "        si = td.Medium(\n",
        "            permittivity=si_n0**2,\n",
        "            conductivity=si_cond,\n",
        "        )\n",
        "\n",
        "    mat_sio2 = td.Medium(permittivity=1.44**2)  # define oxide medium\n",
        "\n",
        "    # define the rib waveguide\n",
        "    rib = td.Structure(\n",
        "        geometry=td.Box.from_bounds(\n",
        "            rmin=[-wg_l_scaled - 10, -wg_w / 2, 0], rmax=[wg_l_scaled + 10, wg_w / 2, wg_h]\n",
        "        ),\n",
        "        medium=si,\n",
        "    )\n",
        "\n",
        "    # define the silicon slab\n",
        "    slab = td.Structure(\n",
        "        geometry=td.Box.from_bounds(\n",
        "            rmin=[-wg_l_scaled - 10, -100, -si_h], rmax=[wg_l_scaled + 10, 100, 0]\n",
        "        ),\n",
        "        medium=si,\n",
        "    )\n",
        "\n",
        "    # define the BOX layer\n",
        "    box = td.Structure(\n",
        "        geometry=td.Box.from_bounds(\n",
        "            rmin=[-wg_l_scaled - 10, -100, -100], rmax=[wg_l_scaled + 10, 100, -si_h]\n",
        "        ),\n",
        "        medium=mat_sio2,\n",
        "    )\n",
        "\n",
        "    # define a mode source\n",
        "    source = td.ModeSource(\n",
        "        center=(-wg_l_scaled / 2, 0, 0),\n",
        "        size=(0, 4, 4),\n",
        "        mode_spec=td.ModeSpec(),\n",
        "        source_time=td.ContinuousWave(\n",
        "            freq0=freq0, fwidth=freq0 / 20, amplitude=np.sqrt(input_power)\n",
        "        ),  # use continuous wave\n",
        "        direction=\"+\",\n",
        "    )\n",
        "\n",
        "    # define a flux time monitor\n",
        "    flux_time = td.FluxTimeMonitor(\n",
        "        center=(wg_l_scaled / 2, 0, 0),\n",
        "        size=(0, 4, 4),\n",
        "        name=\"flux_time\",\n",
        "        interval=1,\n",
        "    )\n",
        "\n",
        "    # define a auxiliary field time monitor to check the auxiliary field which is related to the nonlinear polarizations\n",
        "    aux_field = td.AuxFieldTimeMonitor(\n",
        "        center=flux_time.center, size=(0, 0, 0), fields=[\"Nfy\"], interval=2, name=\"aux_field\"\n",
        "    )\n",
        "\n",
        "    # define a simulation\n",
        "    sim = td.Simulation(\n",
        "        size=(wg_l_scaled + 2, 5, 5),\n",
        "        structures=[box, slab, rib],\n",
        "        monitors=[flux_time, aux_field],\n",
        "        sources=[source],\n",
        "        run_time=2e-12,\n",
        "        normalize_index=None,\n",
        "    )\n",
        "\n",
        "    return sim"
      ],
      "id": "22af4116"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can examine the simulation and the time signal of the CW source.\n",
        "Note: the simulation axes do not have the physical aspect ratio as the\n",
        "simulation is very long along x."
      ],
      "id": "1bdacae6-ba3a-4063-ac9e-4ee4bdacb852"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:16 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Monitor: </span><span style=\"color: #008000; text-decoration-color: #008000\">'aux_field'</span><span style=\"color: #800000; text-decoration-color: #800000\"> </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">(</span><span style=\"color: #800000; text-decoration-color: #800000\">simulation.monitors</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">])</span><span style=\"color: #800000; text-decoration-color: #800000\"> stores field</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'Nfy'</span><span style=\"color: #800000; text-decoration-color: #800000\">, which is not used by any of the nonlinear models present in </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the mediums in the simulation. The resulting data will be zero.    </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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Ro/WLX/yiUfs3b948TZ8+XT/++KM2bdqk1q1bq0uXLurZs6cOP/xw7bnnnvU+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\nTDlYq2pME/3005/YsumCfvr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pk8wkfD/P\nMw/D3HPP+d1zy9ycOfccOHorUJypg88AfRmdellKdV2aq0VpjhYdXBTIS9TApYsSRRlaOPsqIbeQ\nQa4AHDyVyE/W3CpDBXsPBXLi1LB2kkPoAJ1awK27JYrSNeh8v74+7T0USI6sgMJSBk25QEmuFg4e\nShSkaODe2xLFmVo4d1HCsoMcMjmgLhPIS1DDrpMC6VdUcPBUojBVAxsnBZRWgFYFePWzRGGqFh17\nWSDlbAXsPRTIiFZDYQEIAZTl6eDSRYnCNC08Ay1Rlq+Ds58SZQU66NSAVi2QG6+Gg6cS2XFqOPko\nUZKjha2rQqovr3sskZugj//mmXLYdVIgP1EDhYUMCksZygu1cO9ticI0Lbz768uwd1cg44oaWrWA\nTA79PvJWIj9RA5euSpQXCHRwlcPBUwFViYCNkxyZMWo4eCiQcl4Fu04KlOZoobCQwcpBjvICHXzu\ntURBihaefS2RcVmFDq4K5MSroa3QH1uFaVq4dFWiMEV/fKpK9celtaMcZbk6KG1kSL+sgoOnApkx\nath3UkBVooPcQgZHL/1xeddQaxSkaOHgoUDC6XJYO8lRnKmFtkLAylGO4nQt3PtYojBNA89AS2jV\ngLWjPk1JthZW9nJkXVPB0UeJ3HgNHDwV0KoAhQXg1t0CJdladOxuqT8HvRRI/qsC1g5yqEoENBUC\ndu4KFGfqz4+iDA3ce1miKF0LpY1MKsPGSY7cBP35WZCqgZOvEjKZDHIl4N7LEgVpGti6KJAUWQ57\nDyUyY1SwdpBDp9Hvb5e79Mdz5/usUZKlhZOfUn9cWsmgKtahJFun38fJGrj5W+iPSz8lLGxkkMkB\npbUceTfVsOuoQOpFfX3mJ2pg7SSHXCGDpkLA8259HN79rZB5RQV7DyXSLlVArpRBpwHK8rRw9FGi\nME0DjwBLlOToj0u5EtCpAY1KICdODXt3BTKvquHgrURxphYdXOSwtJVDU66D32BrFGVoYe+pQMKf\n+uMyJ04NuVJfF+X5Orh1t0BhmhZefS1RXqCDo7cSeYkaaNUCAJCXoIGDlwK58RrpvOjgLIdtJwXU\nJQIuXZTIuq4/LpMiKmDbUYGiDC0USsDSTo6yfB28+urPQZ97rZCXqIGtmxxZ19TQlAsorGQoSNLA\nubP+euHazUI63l26KFGer4PCSoa0W/WYflkF204KlBfooLCQwdZNgbJ8Lbo+YIP8JA2c79LvKxsn\nOQpTtVCXC1jayVCUduuanKpBx56W+uvorWtRSbYWlnZyZESr4OilRHasvl41FQJypQwuXZQoyb51\nTb5+65ocUQFreznKCnTQlAvYuslRlKGFZ+D/rsmacgGltf68K87UwsZZjuzr+mtyQbIGDt5KCB0g\nVwCegZYoztLC3l2BpMgKOHgqkR6lgrW9HJoK/bHv5Ks/Ln0GWKE4U4OOPSyRFaPSX2MKdCjO1MLW\n7dY1uasSRelaOPkqobSUATLo9+2t4zL5L/13W26CGlb2+uupplygUy/9uds5yBr5N29dk89WQG4h\ng1YlUJKthYOXPv5OvSxQkq2Dk68S1g5yCAFoVQI58fr6S4+6dU1O08DGUQ4LG/310rv//67JyZEV\n+mM4Rg25EpDJgNIcHVy6WqAoTQOPQEuU5+vg6KNERZEOGpWA0AE5cWo4eOm/Px19lCjL1devjbMC\n6jIdvO6xQm6CGvYeStwMLYdtRwUKUzSQKWSwsJahNF8Ljz76a7LvICuUF+hg66r/HtFUCCgs9Me+\no4/+muzcRQlVkYC1kxxOt2Kxca5+TbZ10x/vcoUMNs5ylOXp4DPg1jX5Hv012cZZjrybGqjLBCxt\nZShI+d812a27BTTlAtaOcnRwkeuPyw5ypN26JmfFqGHbUQFNuYBMod+fpTm3rsnJGmlbrR3lKMnR\nQl2qP8aLKq/J6fr7Ip0OsLbX51+UqYWNoxyZMfpjP++mBnYeCggtIFMAnXpaSsda2iX9cZl6vgKW\n9nKoSwXUZQIOHgoUZWrh3c8SRZla/ToZWiitZSjN0aIwXYsOrnLpGlKYpoWjlwJypf566d7bEgWp\nGti6KpAYUQ4HDyWyrqtgZac/P9XlAm7+FihM08BvsDVKsrVw6aJEUmSF/npaLvTXOQ/9fZhbNwsU\nZ+mPfSs7GYTQP4afG6+/V0i9qD/2C1I0sLKXQ2kpg7pcwDNQf+x797NCepT+niX9shqyWyOtlmRr\npe8U9z6WKMvTwclHf8+jqRDQaQWyr+mvDxlX9f+W5upg4yiHlb0c6jL9Nbnyu/7mGf1xmXdTA5lc\nP75jWd6t69St+5GKYh3sb90DqcsE5HIgJ14DRx/9NbnyvtXaUQ57DwVUxQIuXZXIitGXnxRZAVtX\nBYqztZDJAGsH/X2kV19LFKVp4dXPEvlJ+u+N7Fi1/j7UWob8JA1cbl2TXbrcOi4d5HDtpkRpjv5e\nIe2CCg5e+vPc1k2BiiIdZHKZdM/cdbgNClI00ventYP+Gqku1cHKXo7CqtfkHpbQ6QSsbOWADCjO\n1N/Dpkep4OClRM4N/XVLq9bfx7t21e9j7/5WyLr2v221spejolAHVamAXScFitI1+mtyhv4eVqfW\nf+cUZ2pRlKaFjYsc2dduXZNT9eeQTB8CPPtaoihDC0cvJRLDyuHgqURGtAodXOS49zn71vxzyGzI\nhBDC1EFQ25WXlwcXFxcAQG5uLpydnU0cEREREREREVHrM/bfx5wlioiIiIiIiIjIzLDBhoiIiIiI\niIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiI\niIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDB\nhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjIzLDBhoiIiIiIiIjI\nzChNHQC1bTqdTnqfn59vukCIiIiIiIiITKjq38RV/1ZuKjbYULMUFhZK77t27WrCSIiIiIiIiIjM\nQ2FhIVxdXZuVBx+JIiIiIiIiIiIyMzIhhDB1ENR2aTQaJCUlAQAcHBwgl5t3G2B+fr7UE+jGjRtw\ncnIybUDUINxvbRP3W9vE/dY2cb+1TdxvbRP3W9vE/dY2tbX9ptPppKdQfH19oVQ276EmPhJFzaJU\nKtGlSxdTh9EkTk5OcHZ2NnUY1Ejcb20T91vbxP3WNnG/tU3cb20T91vbxP3WNrWV/dbcx6CqMu/u\nEEREREREREREdyA22BARERERERERmRk22BARERERERERmRk22BARERERERERmRk22BARERERERER\nmRk22BARERERERERmRmZEEKYOggiIiIiIiIiIvof9rAhIiIiIiIiIjIzbLAhIiIiIiIiIjIzbLAh\nIiIiIiIiIjIzbLAhIiIiIiIiIjIzbLAhIiIiIiIiIjIzbLAhIiIiIiIiIjIzbLAhIiIiIiIiIjIz\nbLAhIiIiIiIiIjIzbLAhIiIiIiIiIjIzSlMHQHS70tJS/Prrrzhz5gyys7Ph5uaGwYMH44knnoCt\nra1ZldNasbYFxcXF2L17NyIiIpCbmws3NzcEBQVh4sSJsLGxaXb+BQUFCAkJweXLl5GWloasrCzY\n2tqie/fuGD16NPr161fn+tevX8c777zToLKWLFmCgICAZsfcFhQWFmLXrl04e/YscnNz0alTJwwd\nOhQTJkyAtbV1s/KOiorCBx980KC0y5YtQ/fu3U0Wa1uTn5+PnTt34ty5c8jPz0enTp0wfPhwjB8/\nHpaWls3Ke9q0adBqtQ1OHxQUhAULFlT77OzZs1i2bFmD1v/iiy/g6+vbqBjbosLCQkRERCA8PBwX\nL16EWq0GAKxbtw5OTk5GLSsnJwc7duzAxYsXUVBQAA8PDzz00EMYM2YMlMqG3foZI4/2oKCgABER\nEQgLC8Ply5el/bZp0yZ06NDBKGVkZGQgODgYMTExSEtLQ15eHhwcHBAQEIDx48ejW7duda5/4sQJ\n/N///V+DymqJ480c5eXlITw8HOHh4YiKioJGowEA/PLLL5DLm/979W+//YZ169Y1KO2PP/5Y73dU\neno6tm/fjitXrqC4uBienp4YPXo0Hn74YaPE21bk5OQgPDwcYWFhiI6OhlarhaWlJbZs2dLsvFNT\nU/Haa681ap2nnnoKTz31VLXP9uzZgx9//LFB6+/YsaNR5bVFiYmJCA4ORlxcHNLS0lBYWAgXFxf0\n7dsXEyZMgKenp9HL27FjB2JiYlBWVgYfHx+MGzcOw4YNg0wma7U8WpUgMiNHjhwRXl5eAkCNl4eH\nh/jtt9/MppzWirUt2Ldvn+jYsaPBuvD19RUnTpxoct5arVYMGDBAyOVyg/lXvkaOHClu3rxZaz6h\noaF1rl/1dfz48SbH25bs2rVLuLq6GqyDu+66S5w6dapZ+R8/frzBdR4aGmrSWNuSLVu2CEdHR4N1\n0aNHDxEREdGs/BUKRYP3GwDx0ksv1chj3759DV7/0qVLzYrX3J09e1b07t1byGQyg9uflpZm1PLW\nr18v7OzsDJYVEBAgLl682Cp5tHWhoaGiZ8+ete63vLy8Zpdx8+ZN0bt37zrPD7lcLqZPn15neT/8\n8EODzzdjH2/m5tixY6J79+617je1Wm2UclatWtXgOi8qKqozr88//1xYWVkZXHfQoEEiNjbWKDGb\nswMHDoiuXbsarAMrKyujlBEdHd2o7zYA4j//+U+NfJYtW9bg9duz8PBw0aVLlzq338LCQrz++uui\nrKys2eXpdDrxwQcfCKVSabCsESNGiOTk5BbPwxTunJ9IyOydOnUKEyZMQEVFBTp16oQ5c+bA398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38/rl27Zvyg7zBeXl4GG2sA/R98a9askSaf2LZtW7VHMoCmn2/tbXw2cxAXF4dTp04BAMaP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\ncNxUcrlcenSroqKi1nRnz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          }
        }
      ],
      "source": [
        "sim = make_sim(tpa=False, input_power=0.5)\n",
        "\n",
        "fig, ax = plt.subplots(3, 1, figsize=(6, 10), tight_layout=True)\n",
        "sim.plot(y=0, ax=ax[0])\n",
        "ax[0].set_aspect(\"auto\")\n",
        "sim.plot(z=0.2, ax=ax[1])\n",
        "ax[1].set_aspect(\"auto\")\n",
        "sim.sources[0].source_time.plot(times=sim.tmesh, ax=ax[2])\n",
        "plt.show()"
      ],
      "id": "e0a3001e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will first examine the waveguide mode to make sure we have what we\n",
        "expect."
      ],
      "id": "d66117e8-0cb7-49a7-b9b9-c47450ef7082"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:17 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'wg_modes'</span> with resource_id                           \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mos-7d4dcf4b-0935-490e-bed1-e250996e8271'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'MODE'</span>.   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"dc180c266d9f4c3c9155ad39cd04d0b5\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:19 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.004</span>. For this solver type, the        \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>estimate is the final billed cost.                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:20 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"25f95322f3d04feeb6e66430384f8c11\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:30 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:31 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"fa6393feb3104cd7b952800a1dd61e7e\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:34 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "\n",
              "</div>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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CAAAAAECQevfurXfffTeoNj7//HN9/PHHkqTJkydr4sSJ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Viffvqplcd1111X7vp//vMf6/rDDz8c1n6qK1cnLF682MptwoQJ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\nAAAAAHA1iiMAAAAAAMDV/h/9yFHZ18WmEQAAAABJRU5ErkJggg==\n"
          }
        }
      ],
      "source": [
        "ms = td.ModeSimulation(\n",
        "    center=sim.center,\n",
        "    size=sim.size,\n",
        "    structures=sim.structures,\n",
        "    plane=sim.sources[0],\n",
        "    freqs=[freq0],\n",
        "    mode_spec=td.ModeSpec(),\n",
        ")\n",
        "\n",
        "mode_data = web.run(ms, task_name=\"wg_modes\")\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "mode_data.plot_field(field_name=\"Ey\", ax=ax)\n",
        "mode_data.modes.to_dataframe()"
      ],
      "id": "f16a3fab"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Indeed the mode looks like the fundamental waveguide mode, and the mode\n",
        "area of ~1.6 μm$^2$ matches the value given in the paper. Since we\n",
        "shortened the waveguide by a factor of 1000, we need to amplify the\n",
        "linear loss by the same factor to account for it. Here the loss of ~350\n",
        "dB/cm is what we expect (0.35 dB/cm x 1000).\n",
        "\n",
        "Now we will run FDTD simulations with three different values for the\n",
        "carrier lifetime, over an array of input powers."
      ],
      "id": "6bfc27d4-4dc4-4310-9610-7b2a31fe8eb3"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "taus = [65e-9, 10e-9, 1.2e-9]  # carrier life time in seconds\n",
        "powers = [0.01] + np.arange(0.1, 1.1, 0.1).tolist()  # power in watts\n",
        "\n",
        "sim_dict = {}\n",
        "for itau, tau in enumerate(taus):\n",
        "    for ipower, power in enumerate(powers):\n",
        "        sim = make_sim(fca_tau=tau, input_power=power)\n",
        "        sim_dict[f\"tau{itau}_power{ipower}\"] = sim"
      ],
      "id": "0d2c020f"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"14e1e8accd164d72b101352f6349a337\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:13:52 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span> tasks.                      \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:14:17 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.372</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"3f2987a8a6cd4b9792bf3d5e6de2d435\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:18:27 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
            ]
          }
        }
      ],
      "source": [
        "batch = web.Batch(simulations=sim_dict)\n",
        "batch_data = batch.run()"
      ],
      "id": "f8aa859e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can examine the flux-time dependence of the longest-lifetime\n",
        "simulation at the highest input power."
      ],
      "id": "62c555ad-5e7d-415c-b0f9-0f832104fa67"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">07:18:31 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation final field decay value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.98</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater than</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the simulation shutoff threshold of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">. Consider running the    </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">simulation again with a larger </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> duration for more        </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">accurate results.                                                  </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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df/31GT9+/Bu2u+CCCwRWAAAAQKtRdYHVqlWrcvbZZ+fee+9Nkpxyyik5/vjj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\nGDCgaZ+rLdWxY8dylggAAABAwZU1sEp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YG264IUmy55575thjj82SJUtyxx13ZNGiRamp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\n"
          }
        }
      ],
      "source": [
        "sim_data = batch_data[\"tau0_power8\"]\n",
        "sim_data[\"flux_time\"].flux.plot()\n",
        "plt.show()"
      ],
      "id": "7456501b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Similarly we can plot the auxiliary field profile, which is related to\n",
        "the nonlinear polarizations, and see if it has reached steady state.\n",
        "Here we plot `Nfy` since the dominant field is in the $y$ direction.\n",
        "Different components don’t interact nonlinearly as a modeling\n",
        "approximation"
      ],
      "id": "af5479bd-af80-4b29-9442-e2fccc1bed0d"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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4T6Tqupq0nhMAAADORpAEAIBNmD0jaWjf3ZPGfq3YlNZzAgAAwNkI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          }
        }
      ],
      "source": [
        "sim_data[\"aux_field\"].Nfy.plot()\n",
        "plt.show()"
      ],
      "id": "b8bd4723"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The output power is just the time-average of the time-dependent flux\n",
        "once steady-state is reached."
      ],
      "id": "beb5468b-d7be-4d6d-a4cb-25919639dad0"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CW flux at output:  0.07500637"
          ]
        }
      ],
      "source": [
        "ts = sim.tmesh > 1.5e-12\n",
        "power = np.mean(sim_data[\"flux_time\"].flux[ts]).values\n",
        "print(\"CW flux at output: \", power)"
      ],
      "id": "e244168c"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here we turn off the warnings by setting the logging level to `\"ERROR\"`.\n",
        "This is to prevent many repeated warnings generated from the parameter\n",
        "sweep. In this case, we will get warnings about the field decay not\n",
        "meeting the shutoff condition. Since we inject a CW source, the field\n",
        "will never decay so this is expected. In general, however, we highly\n",
        "advise against turning the warnings off since they can indicate real\n",
        "issues regarding the simulation setup."
      ],
      "id": "0659ff6c-5672-4119-b9c1-bf8f470fcc21"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "td.config.logging.level = \"ERROR\""
      ],
      "id": "e2c6a193"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can compute this output power for all values of $\\tau$ and the\n",
        "input power, and recreate Fig. 2 from the\n",
        "[paper](https://doi.org/10.1038/nature03273). The simulation results\n",
        "here closely match the experimental measurements."
      ],
      "id": "38e7bcdd-c670-4541-8832-5ae806f01145"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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iXhT4nI3LN5bv/9TQpiH3fyI5hlFEREREREREpVzGVVHp0ldHTW81HZffXsbx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\noujr0QqZHqJL8ZfS1IomSGHTpTJxsoVQ77yTZsyPP/6orl276tq1a4bzh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zZs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\nERERERGR3bAYRUREREREREREdvP/AHCaOdS13ha5AAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1)\n",
        "\n",
        "# colors and markers for styling the plot\n",
        "colors = [\"black\", \"red\", \"green\"]\n",
        "markers = [\"s\", \"D\", \"^\"]\n",
        "\n",
        "# result plotting\n",
        "for itau, tau in enumerate(taus):\n",
        "    output_powers = []\n",
        "    for ipower, power in enumerate(powers):\n",
        "        output_powers.append(\n",
        "            np.mean(batch_data[f\"tau{itau}_power{ipower}\"][\"flux_time\"].flux[ts]).values\n",
        "        )\n",
        "    ax.plot(\n",
        "        np.array(powers) * 1000,\n",
        "        np.array(output_powers) * 1000,\n",
        "        marker=markers[itau],\n",
        "        markersize=6,\n",
        "        label=f\"Lifetime = {tau * 1e9:.2g} ns\",\n",
        "        c=colors[itau],\n",
        "        linewidth=2,\n",
        "    )\n",
        "\n",
        "ax.legend()\n",
        "ax.set_xlabel(\"Input power (mW)\")\n",
        "ax.set_ylabel(\"Output power (mW)\")\n",
        "plt.show()"
      ],
      "id": "0fe98eef"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}