{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77a6ec47-b8ac-42ea-92c4-cd07f09991b2",
   "metadata": {},
   "source": [
    "# Two-photon absorption (TPA) and free-carrier absorption (FCA) in a Si waveguide\n",
    "\n",
    "In this notebook, we will reproduce part of the experimental results in `Rong, H., Liu, A., Jones, R. et al. An all-silicon Raman laser. Nature 433, 292–294 (2005)` [DOI: 10.1038/nature03273](https://doi.org/10.1038/nature03273). Specifically, the authors provided experimental results for the input-output power relationship for a 4.8cm long rib waveguide, with the output power following a non-linear dependence on the input power due to two-photon absorption (TPA) and free-carrier absorption (FCA) effects. All of the physical parameters are provided in the paper which makes for a great benchmark to reproduce with the first-principle TPA and FCA 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\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce134e60-d42e-4817-83d5-c16e503137af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "from tidy3d import web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bfd052-f17b-4b5e-a1ca-ac719f7b8401",
   "metadata": {},
   "source": [
    "First, define some fundamental parameters as they appear in the paper. It also provides the nonlinear parameters like the TPA coefficient and the FCA optical cross-section. The carrier lifetime $\\tau$ on the other hand is controlled by placing the waveguide in a p-i-n junction and controlling the voltage across the junction, which can help sweep out free carriers faster. We are going to model three different carrier lifetimes, as presented in Fig. 2 of the [paper](https://doi.org/10.1038/nature03273). The paper also reports a linear waveguide loss of 0.35 dB/cm. We will account for it by adding an imaginary part of the  permittivity, or equivalently, a finite conductivity to the silicon medium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35e11844-68ae-405b-86d3-88b4012fc5e0",
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bfedb0-3d90-4974-bfb3-6f56c50ab039",
   "metadata": {},
   "source": [
    "The TPA + FCA modeling in Tidy3D follows these equations:\n",
    "\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",
    "\n",
    "where $P$ denote various polarization densities, and $N_f$ is the free carrier concentration. The factors of $\\frac{4}{3}$ and $\\frac{8}{3}$ in the equations are coming from using real fields instead of complex fields in the solver. More details can be found on this [study](https://ieeexplore.ieee.org/document/4299014).\n",
    "\n",
    "We will be looking at continuous-wave operation, in which we evolve the system under a continuous-wave source until a steady state is reached both for the optical oscillations and the free-carrier distribution. In that regime, $\\frac{dN_f}{dt} = 0$, and\n",
    "\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",
    "\n",
    "This means that the free-carrier polarization density $P_{FCA}$ is proportional to $\\sigma \\tau$, which allows us to make a simplifying adjustment to be able to run the FDTD simulation within a reasonable number of time steps. Namely, the typical $\\tau$ in that system is on the order of nanoseconds, which is much larger than the optical period, and too long for the FDTD simulation. However, we can simultaneously decrease $\\tau$ by a factor and increase $\\sigma$ by the same factor, such that the steady-state concentration remains the same.\n",
    "\n",
    "For this illustration, we also will not be simulating the whole 4.8cm waveguide from the paper. We can scale the TPA coefficient $\\beta$, making the overall nonlinearity stronger, while correspondingly shortening the waveguide. This is a valid modification to the setup since both the two-photon absorption, and the free-carrier concentration $N_f$, and thus the free-carrier absorption, are proportional to $\\beta$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad2e8d80-2430-4e1a-a539-4b39c8f7aa20",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7976313e-83b0-4818-8c2f-2dcfe823123b",
   "metadata": {},
   "source": [
    "Now we can define a function to generate the simulation. Note that we will not be including the doped silicon regions and the electrodes, as the authors say that they have verified that their introduction does not affect the light propagation in the linear regime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6952265d-77a6-490e-a65d-4d988e20064a",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46ea1bd-41b3-423e-8d10-ce2db5590f17",
   "metadata": {},
   "source": [
    "We can examine the simulation and the time signal of the CW source. Note: the simulation axes do not have the physical aspect ratio as the simulation is very long along x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afe9f463-7be1-477b-bc24-ade0bac088a9",
   "metadata": {},
   "outputs": [
    {
     "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:53:09 CEST </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     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">field </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     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">present in the mediums in the simulation. The resulting data will </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">be zero.                                                          </span>\n",
       "</pre>\n"
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       "\u001b[2;36m11:53:09 CEST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Monitor: \u001b[0m\u001b[32m'aux_field'\u001b[0m\u001b[31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[31msimulation.monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;31m]\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m stores     \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mfield \u001b[0m\u001b[32m'Nfy'\u001b[0m\u001b[31m, which is not used by any of the nonlinear models     \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mpresent in the mediums in the simulation. The resulting data will \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mbe zero.                                                          \u001b[0m\n"
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",
      "text/plain": [
       "<Figure size 600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8d0ab4-fe41-465b-9d62-587b979828b0",
   "metadata": {},
   "source": [
    "We will first examine the waveguide mode to make sure we have what we expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "018fd5f4-69e8-411b-8448-9cd10e08ae26",
   "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\">11:53:11 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'wg_modes'</span> with task_id                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'mos-662a9f6c-8bc6-415c-9377-4a8558c3e91f'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'MODE'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:53:11 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'wg_modes'\u001b[0m with task_id                              \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'mos-662a9f6c-8bc6-415c-9377-4a8558c3e91f'\u001b[0m and task_type \u001b[32m'MODE'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13dc475255e84c2a84a51a77489d4ee5",
       "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\">11:53:14 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.004</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;36m11:53:14 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.004\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\">11:53:15 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:53:15 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38fda901d0c04430983c69b41b062d5b",
       "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\">11:53:18 CEST </span>loading simulation from simulation_data.hdf5                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:53:18 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>n eff</th>\n",
       "      <th>k eff</th>\n",
       "      <th>loss (dB/cm)</th>\n",
       "      <th>TE (Ey) fraction</th>\n",
       "      <th>wg TE fraction</th>\n",
       "      <th>wg TM fraction</th>\n",
       "      <th>mode area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <th>mode_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.934145e+14</th>\n",
       "      <th>0</th>\n",
       "      <td>1.55</td>\n",
       "      <td>3.41443</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>353.415992</td>\n",
       "      <td>0.999346</td>\n",
       "      <td>0.98887</td>\n",
       "      <td>0.981245</td>\n",
       "      <td>1.627082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         wavelength    n eff     k eff  loss (dB/cm)  \\\n",
       "f            mode_index                                                \n",
       "1.934145e+14 0                 1.55  3.41443  0.001004    353.415992   \n",
       "\n",
       "                         TE (Ey) fraction  wg TE fraction  wg TM fraction  \\\n",
       "f            mode_index                                                     \n",
       "1.934145e+14 0                   0.999346         0.98887        0.981245   \n",
       "\n",
       "                         mode area  \n",
       "f            mode_index             \n",
       "1.934145e+14 0            1.627082  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7de060-612a-4114-bb93-75644dfc3901",
   "metadata": {},
   "source": [
    "Indeed the mode looks like the fundamental waveguide mode, and the mode area of ~1.6 μm$^2$ matches the value given in the paper. Since we shortened the waveguide by a factor of 1000, we need to amplify the linear loss by the same factor to account for it. Here the loss of ~350 dB/cm is what we expect (0.35 dB/cm x 1000)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e26440-5004-4c04-8f04-51b7cbe444fc",
   "metadata": {},
   "source": [
    "Now we will run FDTD simulations with three different values for the carrier lifetime, over an array of input powers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d04478bf-e30a-40ca-9e76-f4c534f4066f",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76195793-420c-4b82-a599-7c900e8b0a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b631e073006a42c4840526b1f658a0fa",
       "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\">11:53:30 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span> tasks.                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:53:30 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m33\u001b[0m tasks.                     \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\">11:54:02 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.367</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:54:02 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m7.367\u001b[0m for the whole batch.               \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>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>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b70d0807bcea48389fe33454649fad1b",
       "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\">11:54:27 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:54:27 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f7347d3b82c4426a4e49be49245a768",
       "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"
    }
   ],
   "source": [
    "batch = web.Batch(simulations=sim_dict)\n",
    "batch_data = batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a820bb-a89d-4ad1-9aba-396d6c06e1aa",
   "metadata": {},
   "source": [
    "We can examine the flux-time dependence of the longest-lifetime simulation at the highest input power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56c355a9-d66d-456d-af97-b47d1e9dc557",
   "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\">11:55:00 CEST </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\">0.983</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">than 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  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #800000; text-decoration-color: #800000\">the 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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:55:00 CEST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m0.983\u001b[0m\u001b[31m is greater   \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running  \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more   \u001b[0m\n",
       "\u001b[2;36m              \u001b[0m\u001b[31maccurate results.                                                 \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = batch_data[\"tau0_power8\"]\n",
    "sim_data[\"flux_time\"].flux.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43280736-a887-43a7-8110-70bfb1259ffa",
   "metadata": {},
   "source": [
    "Similarly we can plot the auxiliary field profile, which is related to the nonlinear polarizations, and see if it has reached steady state. Here we plot `Nfy` since the dominant field is in the $y$ direction. Different components don't interact nonlinearly as a modeling approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f7ba8dc-0f02-4304-b266-aca1bb8f1651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data[\"aux_field\"].Nfy.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6af55af-61c4-43da-849e-3fdfbfdac951",
   "metadata": {},
   "source": [
    "The output power is just the time-average of the time-dependent flux once steady-state is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72d2cad6-23c4-4cc2-9412-32b594231ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CW flux at output:  0.07502077\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89231786-369f-49e9-bc3e-512a9c34d1e4",
   "metadata": {},
   "source": [
    "Here we turn off the warnings by setting the logging level to `\"ERROR\"`. This is to prevent many repeated warnings generated from the parameter sweep. In this case, we will get warnings about the field decay not meeting the shutoff condition. Since we inject a CW source, the field will never decay so this is expected. In general, however, we highly advise against turning the warnings off since they can indicate real issues regarding the simulation setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0306d774-e571-418f-874f-94c4de047909",
   "metadata": {},
   "outputs": [],
   "source": [
    "td.config.logging.level = \"ERROR\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6199178f-4879-48a8-86c5-5f8b24e4311d",
   "metadata": {},
   "source": [
    "Now we can compute this output power for all values of $\\tau$ and the input power, and recreate Fig. 2 from the [paper](https://doi.org/10.1038/nature03273). The simulation results here closely match the experimental measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "952105a1-4b9f-416b-82dc-a69fc0e5c274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fada12ff-d696-4691-b0aa-e0e6a7a25e3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components",
   "Active photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to model two photon absorption (TPA) and free carrier absorption (FCA) in a silicon waveguide in Tidy3D FDTD.",
  "feature_image": "./img/TPA_FCA.png",
  "features": [
   "Parameter sweep",
   "Nonlinear material",
   "Mode analysis"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "two photon absorption, TPA, FCA, free carrier absorption, silicon, photonics, waveguide, 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": "3.11.0"
  },
  "title": "TPA and FCA in a Si waveguide in Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}