{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "781520ab",
   "metadata": {},
   "source": [
    "# Avalanche photodiode\n",
    "\n",
    "This notebook demonstrates the modeling of an on-chip avalanche photodiode (APD). These photodetectors provide internal signal amplification by operating under high reverse bias and are used in many applications where it is necessary to detect very weak optical signals.\n",
    "\n",
    "The internal gain in an APD arises from impact ionization, which occurs when the electric field is strong enough for charge carriers to gain sufficient kinetic energy to ionize atoms and create additional electron–hole pairs.\n",
    "\n",
    "The device is inspired by the work of `Zhihong Huang, Cheng Li, Di Liang, Kunzhi Yu, Charles Santori, Marco Fiorentino, Wayne Sorin, Samuel Palermo, and Raymond G. Beausoleil, \"25 Gbps low-voltage waveguide Si–Ge avalanche photodiode,\" Optica 3(8), (2016).` [DOI: https://doi.org/10.1364/OPTICA.3.000793](https://doi.org/10.1364/OPTICA.3.000793). It consists of a rib Si waveguide with n–p–i doping, the Ge region p- and pp-doped, and aluminum contacts.\n",
    "\n",
    "The general workflow is as follows:\n",
    "\n",
    "[1.](1) **Define multiphysics media**\n",
    "\n",
    " We will define both the optical and charge properties of the materials, including doping, and models for band-to-band tunneling and impact ionization.\n",
    "\n",
    "[2.](2) **Run the optical simulation**\n",
    "\n",
    " We will use [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to calculate the waveguide modes.\n",
    "\n",
    "[3.](3) **Calculate optical absorption and carrier generation**\n",
    "\n",
    " With the field profile information, we will calculate the volumetric absorbed power:\n",
    "\n",
    " $P_{abs} = \\tfrac{1}{2} \\, \\omega \\, \\varepsilon_0 \\, \\varepsilon'' \\, |E|^2$,\n",
    "\n",
    " where $\\varepsilon_0$ is the vacuum permittivity, and $\\varepsilon''$ is the imaginary part of the complex permittivity.\n",
    "\n",
    " Next, we compute the electron–hole pair generation rate:\n",
    "\n",
    " $g = \\tfrac{P_{abs}}{\\hbar\\omega q} \\ \\text{[1/(s µm}^3)]$\n",
    "\n",
    " where $q$ is the electron charge.\n",
    "\n",
    "[4.](4) **Charge simulation**\n",
    "\n",
    " Finally, we will use the carrier generation data in a charge simulation to calculate the dark current and bright current.\n",
    "\n",
    "\n",
    "<img src=\"img/APD.png\" width=\"500\" alt=\"Schematic\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf86873e-40a4-487d-8828-a24386cd6aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tidy3d as td\n",
    "from matplotlib import pyplot as plt\n",
    "from tidy3d import web\n",
    "\n",
    "# Prevent warning messages relevant only for FDTD simulations\n",
    "td.config.logging.level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec729cba",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cfe1bc-3023-4f9c-a167-c4d53dc06ba4",
   "metadata": {},
   "source": [
    "First we define global parameters used in the simulation.\n",
    "\n",
    "Since the structure is symmetric to x = 0. we're modeling only the right half of the device. With this in mind, width is the width of half of the component, i.e., the half being modeled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee616f48-32a0-450a-893c-d0210277d0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: all dimensions in um\n",
    "\n",
    "z_size = 1\n",
    "\n",
    "# si_bottom\n",
    "si_b_w = 4.4  # width\n",
    "si_b_h = 0.35 / 5  # height\n",
    "\n",
    "# si_top\n",
    "si_t_w = 2.2  # width\n",
    "si_t_h = 0.35 * 4 / 5  # height\n",
    "\n",
    "# Ge\n",
    "ge_b_w = 1.8  # bottom width\n",
    "ge_t_w = 1.15  # top width\n",
    "ge_h = 0.35  # height\n",
    "\n",
    "# SiO2 padding\n",
    "sio2_w = si_b_w\n",
    "sio2_h = 0.6\n",
    "\n",
    "# emitter\n",
    "emitter_w = 1  # width\n",
    "emitter_h = 0.6  # height\n",
    "\n",
    "# collector\n",
    "collector_w = 0.5  # width\n",
    "collector_h = 0.6  # height\n",
    "\n",
    "total_h = 2 * sio2_h + si_b_h + si_t_h + ge_h\n",
    "\n",
    "# Temperature (K)\n",
    "T0 = 280\n",
    "\n",
    "# structure overlap\n",
    "s_ol = 1e-8\n",
    "\n",
    "wvl_um = 1.55\n",
    "freq0 = td.C_0 / wvl_um\n",
    "P_in = 3e-4  # guided mode power [W]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73cbd36f",
   "metadata": {},
   "source": [
    "### Defining Media\n",
    "\n",
    "Now, we will define the media for the multiphysics simulations.\n",
    "\n",
    "The Si and Ge will be modeled with the [SemiconductorMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SemiconductorMedium.html) class.\n",
    "\n",
    "To accurately simulate the APD behavior, we will add to the semiconductor media a band-to-band tunneling model, defined with the [HurkxDirectBandToBandTunneling](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.HurkxDirectBandToBandTunneling.html) object, and an impact ionization model, defined with the [SelberherrImpactIonization](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SelberherrImpactIonization.html) object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46d82c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "intrinsic_si = td.SemiconductorMedium(\n",
    "    permittivity=11.7,\n",
    "    N_c=td.IsotropicEffectiveDOS(m_eff=1.18),\n",
    "    N_v=td.IsotropicEffectiveDOS(m_eff=0.8098),\n",
    "    E_g=td.VarshniEnergyBandGap(eg_0=1.16, alpha=4.73e-4, beta=636),\n",
    "    mobility_n=td.CaugheyThomasMobility(\n",
    "        mu_min=52.2,\n",
    "        mu=1471.0,\n",
    "        ref_N=9.68e16,\n",
    "        exp_N=0.68,\n",
    "        exp_1=-0.57,\n",
    "        exp_2=-2.33,\n",
    "        exp_3=2.4,\n",
    "        exp_4=-0.146,\n",
    "    ),\n",
    "    mobility_p=td.CaugheyThomasMobility(\n",
    "        mu_min=44.9,\n",
    "        mu=470.5,\n",
    "        ref_N=2.23e17,\n",
    "        exp_N=0.719,\n",
    "        exp_1=-0.57,\n",
    "        exp_2=-2.33,\n",
    "        exp_3=2.4,\n",
    "        exp_4=-0.146,\n",
    "    ),\n",
    "    R=[\n",
    "        td.ShockleyReedHallRecombination(\n",
    "            tau_n=td.FossumCarrierLifetime(\n",
    "                tau_300=1e-11, alpha_T=0, A=1, B=0, C=1, N0=7.1e15, alpha=1\n",
    "            ),\n",
    "            tau_p=td.FossumCarrierLifetime(\n",
    "                tau_300=1e-11, alpha_T=0, A=1, B=0, C=0, N0=7.1e15, alpha=1\n",
    "            ),\n",
    "        ),\n",
    "        td.RadiativeRecombination(r_const=1.6e-14),\n",
    "        td.AugerRecombination(c_n=2.8e-31, c_p=9.9e-32),\n",
    "        td.HurkxDirectBandToBandTunneling(A=4e14, B=1.9e7, E_0=1, sigma=2.5),\n",
    "        td.SelberherrImpactIonization(\n",
    "            alpha_n_inf=7.03e5,\n",
    "            alpha_p_inf=1.582e6,\n",
    "            E_n_crit=1.23e6,\n",
    "            E_p_crit=2.03e6,\n",
    "            beta_n=1,\n",
    "            beta_p=1,\n",
    "        ),\n",
    "    ],\n",
    "    delta_E_g=td.SlotboomBandGapNarrowing(\n",
    "        v1=9 * 1e-3,\n",
    "        n2=1e17,\n",
    "        c2=0.5,\n",
    "        min_N=1e15,\n",
    "    ),\n",
    ")\n",
    "\n",
    "intrinsic_ge = td.SemiconductorMedium(\n",
    "    permittivity=16,\n",
    "    N_c=td.IsotropicEffectiveDOS(m_eff=0.56),\n",
    "    N_v=td.IsotropicEffectiveDOS(m_eff=0.29),\n",
    "    E_g=td.VarshniEnergyBandGap(eg_0=0.7437, alpha=0.0004774, beta=235),\n",
    "    mobility_n=td.CaugheyThomasMobility(\n",
    "        mu_min=850,\n",
    "        mu=3900,\n",
    "        ref_N=2.6e17,\n",
    "        exp_N=0.56,\n",
    "        exp_1=0,\n",
    "        exp_2=-1.66,\n",
    "        exp_3=0,\n",
    "        exp_4=0,\n",
    "    ),\n",
    "    mobility_p=td.CaugheyThomasMobility(\n",
    "        mu_min=300,\n",
    "        mu=1800,\n",
    "        ref_N=1e17,\n",
    "        exp_N=1,\n",
    "        exp_1=0,\n",
    "        exp_2=-2.33,\n",
    "        exp_3=0,\n",
    "        exp_4=0,\n",
    "    ),\n",
    "    R=[\n",
    "        td.ShockleyReedHallRecombination(\n",
    "            tau_n=td.FossumCarrierLifetime(\n",
    "                tau_300=1e-10, alpha_T=0, A=1, B=0, C=0, N0=1e16, alpha=1\n",
    "            ),\n",
    "            tau_p=td.FossumCarrierLifetime(\n",
    "                tau_300=1e-10, alpha_T=0, A=1, B=0, C=0, N0=1e16, alpha=1\n",
    "            ),\n",
    "        ),\n",
    "        td.RadiativeRecombination(r_const=6.41e-14),\n",
    "        td.AugerRecombination(c_n=1e-30, c_p=1e-30),\n",
    "        td.HurkxDirectBandToBandTunneling(A=4e14, B=1.9e7, E_0=1, sigma=2.5),\n",
    "        td.SelberherrImpactIonization(\n",
    "            alpha_n_inf=4.9e5,\n",
    "            alpha_p_inf=2.15e5,\n",
    "            E_n_crit=7.9e5,\n",
    "            E_p_crit=7.1e5,\n",
    "            beta_n=1,\n",
    "            beta_p=1,\n",
    "        ),\n",
    "    ],\n",
    "    delta_E_g=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f596fe67-0c72-4ea9-9eee-0749dbb99f38",
   "metadata": {},
   "source": [
    "### Doping\n",
    "\n",
    "Next, we will define the doping regions for both Si and Ge. We will use a [GaussianDoping](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianDoping.html) object, which creates a Gaussian doping distribution that better reflects real doping profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0879388e-2070-4694-874f-1ef40e7288d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_Si = td.GaussianDoping.from_bounds(\n",
    "    concentration=1e19,\n",
    "    rmin=(-si_b_w, 0 - s_ol, -z_size),\n",
    "    rmax=(si_b_w, 0.22, z_size),\n",
    "    ref_con=1e16,\n",
    "    width=0.05,\n",
    "    source=\"ymin\",\n",
    ")\n",
    "bg_Si = td.ConstantDoping.from_bounds(\n",
    "    concentration=1e16, rmin=(-si_b_w, 0, -z_size), rmax=(si_b_w, si_b_h + si_t_h + s_ol, z_size)\n",
    ")\n",
    "p_Si = td.ConstantDoping.from_bounds(\n",
    "    concentration=2e17,\n",
    "    rmin=(-si_b_w, si_b_h + si_t_h - 0.05, -z_size),\n",
    "    rmax=(si_b_w, si_b_h + si_t_h + s_ol, z_size),\n",
    ")\n",
    "\n",
    "p_Ge = td.GaussianDoping.from_bounds(\n",
    "    rmin=(-ge_b_w, si_b_h + si_t_h, -z_size),\n",
    "    rmax=(ge_b_w, si_b_h + si_t_h + ge_h, z_size),\n",
    "    ref_con=1e17,\n",
    "    concentration=1e18,\n",
    "    width=0.35,\n",
    "    source=\"ymax\",\n",
    ")\n",
    "\n",
    "pp_Ge = td.GaussianDoping.from_bounds(\n",
    "    rmin=(-ge_b_w, si_b_h + si_t_h + ge_h - 0.02 - 0.02, -z_size),\n",
    "    rmax=(1.8, si_b_h + si_t_h + ge_h, z_size),\n",
    "    ref_con=1e16,\n",
    "    concentration=2e19,\n",
    "    width=0.02,\n",
    "    source=\"ymax\",\n",
    ")\n",
    "\n",
    "\n",
    "doped_Si = intrinsic_si.updated_copy(N_a=[bg_Si, p_Si], N_d=[n_Si])\n",
    "doped_Ge = intrinsic_ge.updated_copy(N_a=[p_Ge, pp_Ge])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c540e36-c9ca-4528-8ca9-827fcaa59853",
   "metadata": {},
   "source": [
    "### Defining the Multiphysics Medium\n",
    "\n",
    "Finally, we can combine the optical and charge material properties into a [MultiPhysicsMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MultiPhysicsMedium.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7f5284c-d485-442d-ba24-98d2f0b9881a",
   "metadata": {},
   "outputs": [],
   "source": [
    "doped_Si = intrinsic_si.updated_copy(N_a=[bg_Si, p_Si], N_d=[n_Si])\n",
    "doped_Ge = intrinsic_ge.updated_copy(N_a=[p_Ge, pp_Ge])\n",
    "\n",
    "Si = td.MultiPhysicsMedium(\n",
    "    charge=doped_Si,\n",
    "    optical=td.material_library[\"cSi\"][\"Palik_LowLoss\"],\n",
    "    name=\"Si\",\n",
    ")\n",
    "\n",
    "Ge = td.MultiPhysicsMedium(\n",
    "    charge=doped_Ge,\n",
    "    optical=td.material_library[\"Ge\"][\"Nunley\"],\n",
    "    name=\"Ge\",\n",
    ")\n",
    "\n",
    "SiO2 = td.MultiPhysicsMedium(\n",
    "    charge=td.ChargeInsulatorMedium(permittivity=3.9),\n",
    "    optical=td.material_library[\"SiO2\"][\"Horiba\"],\n",
    "    name=\"SiO2\",\n",
    ")\n",
    "\n",
    "Al = td.MultiPhysicsMedium(\n",
    "    charge=td.ChargeConductorMedium(conductivity=1),\n",
    "    optical=td.material_library[\"Al\"][\"RakicLorentzDrude1998\"],\n",
    "    name=\"Al\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afde860f-4a9d-4c0a-9740-73ab28c43aea",
   "metadata": {},
   "source": [
    "### Structures\n",
    "\n",
    "Now, we can define our geometries, assign material properties, and define the [Structure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf161040-753d-4dbc-803d-d76459c5ee98",
   "metadata": {},
   "outputs": [],
   "source": [
    "si_bottom = td.Structure(\n",
    "    geometry=td.Box.from_bounds(rmin=(-si_b_w, 0, -z_size), rmax=(si_b_w, si_b_h, z_size)),\n",
    "    medium=Si,\n",
    "    name=\"si_bottom\",\n",
    ")\n",
    "\n",
    "si_top = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-si_t_w, si_b_h - s_ol, -z_size), rmax=(si_t_w, si_b_h + si_t_h + s_ol, z_size)\n",
    "    ),\n",
    "    medium=Si,\n",
    "    name=\"si_top\",\n",
    ")\n",
    "\n",
    "vertices = np.array(\n",
    "    [\n",
    "        (-ge_b_w, si_b_h + si_t_h),\n",
    "        (ge_b_w, si_b_h + si_t_h),\n",
    "        (ge_t_w, si_b_h + si_t_h + ge_h),\n",
    "        (-ge_t_w, si_b_h + si_t_h + ge_h),\n",
    "    ]\n",
    ")\n",
    "ge_struct = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-z_size, z_size)),\n",
    "    medium=Ge,\n",
    "    name=\"ge_struct\",\n",
    ")\n",
    "\n",
    "sio2_struct = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-sio2_w, -sio2_h, -z_size), rmax=(sio2_w, si_b_h + si_t_h + ge_h + sio2_h, z_size)\n",
    "    ),\n",
    "    medium=SiO2,\n",
    "    name=\"sio2_struct\",\n",
    ")\n",
    "\n",
    "emitter = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(si_b_w - emitter_w, si_b_h - s_ol, -z_size), rmax=(si_b_w, si_b_h + emitter_h, z_size)\n",
    "    ),\n",
    "    medium=Al,\n",
    "    name=\"emitter\",\n",
    ")\n",
    "\n",
    "collector = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-collector_w, si_b_h + si_t_h + ge_h - s_ol, -z_size),\n",
    "        rmax=(collector_w, si_b_h + si_t_h + ge_h + collector_h, z_size),\n",
    "    ),\n",
    "    medium=Al,\n",
    "    name=\"collector\",\n",
    ")\n",
    "\n",
    "structures = [sio2_struct, si_bottom, si_top, ge_struct, emitter, collector]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f3a54e-b064-4d7d-835b-fe13d15fdaf4",
   "metadata": {},
   "source": [
    "For easier communication between the optical and charge solvers, we can create a [Scene](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Scene.html) object containing all the structures and the background medium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b694d36-5d76-410f-a9eb-1a2b2d429153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scene = td.Scene(\n",
    "    structures=structures,\n",
    "    medium=td.MultiPhysicsMedium(heat=td.FluidMedium()),\n",
    ")\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(10, 10))\n",
    "scene.plot(z=0, ax=ax[0])\n",
    "scene.plot_structures_property(z=0, property=\"doping\", ax=ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f01d42-8a1b-45fc-9c35-1b72c13591d1",
   "metadata": {},
   "source": [
    "## Optical Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a460099-348f-4fd7-92d2-0aa002038692",
   "metadata": {},
   "source": [
    "### Optical Structures\n",
    "\n",
    "Since the mode solver doesn't yet support [MultiPhysicsMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MultiPhysicsMedium.html), we'll recreate these structures using only their optical properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6cf1b70-3638-41ba-8e7f-2d94186fe29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_structs = [struct.updated_copy(medium=struct.medium.optical) for struct in structures]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9aaad4-3c84-4482-9173-4246db070d7c",
   "metadata": {},
   "source": [
    "### Defining the `ModeSolver` Object\n",
    "\n",
    "Now, we will create a [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) object to run mode analysis and obtain field data. We will also add a [PermittivityMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PermittivityMonitor.html) to calculate the optical absorption and generation rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc16e200-c892-4d9b-935c-bd6a51a50866",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Permittivity monitor\n",
    "perm_mnt = td.PermittivityMonitor(\n",
    "    size=(2 * si_b_w, total_h, 0),\n",
    "    freqs=[freq0],\n",
    "    name=\"permittivity\",\n",
    ")\n",
    "\n",
    "# Cross-section simulation (x-size=0), periodic bc OK for mode extraction\n",
    "opt_grid = td.GridSpec.auto(min_steps_per_wvl=30, wavelength=wvl_um)\n",
    "opt_sim = td.Simulation(\n",
    "    size=(2 * si_b_w, total_h, 0),\n",
    "    center=(0, total_h / 2 - sio2_h / 2, 0),\n",
    "    structures=opt_structs,\n",
    "    medium=SiO2.optical,\n",
    "    run_time=1e-15,\n",
    "    grid_spec=opt_grid,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(td.Periodic()),\n",
    ")\n",
    "\n",
    "# Mode plane through the core\n",
    "from tidy3d.plugins.mode import ModeSolver\n",
    "from tidy3d.plugins.mode.web import run as run_mode\n",
    "\n",
    "mode_plane = td.Box.from_bounds(\n",
    "    rmin=(-si_b_w, -sio2_h, 0), rmax=(si_b_w, si_b_h + si_t_h + ge_h + 1, 0)\n",
    ")\n",
    "ms = td.ModeSimulation.from_simulation(\n",
    "    simulation=opt_sim,\n",
    "    plane=mode_plane,\n",
    "    mode_spec=td.ModeSpec(num_modes=1),\n",
    "    freqs=[freq0],\n",
    "    colocate=False,\n",
    "    fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "    monitors=[perm_mnt],\n",
    ")\n",
    "ms.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be09560",
   "metadata": {},
   "source": [
    "Running the mode solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4da64a8-437e-4037-9bda-64c0deaf1993",
   "metadata": {
    "scrolled": true
   },
   "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\">11:19:14 CEST </span>Loading simulation from local cache. View cached task using web UI\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>at                                                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d2</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">3-4e75-9b0e-158db328c908'</span></a>.                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:19:14 CEST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI\n",
       "\u001b[2;36m              \u001b[0mat                                                                \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=673831;https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=236113;https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=673831;https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=275174;https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\u001b\\\u001b[32mmos\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=673831;https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\u001b\\\u001b[32m-8cbe804c-9d2\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=673831;https://tidy3d.simulation.cloud/workbench?taskId=mos-8cbe804c-9d23-4e75-9b0e-158db328c908\u001b\\\u001b[32m3-4e75-9b0e-158db328c908'\u001b[0m\u001b]8;;\u001b\\.                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_data = web.run(ms, task_name=\"APD_opt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e615511-c640-43fe-8cf3-79151a6253c3",
   "metadata": {},
   "source": [
    "### Mode Solver Results\n",
    "\n",
    "First, we can visualize the field profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00599638-9900-4478-ae48-ab27c2d9e576",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_eff: 4.3389294168963595\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize mode fields (Ex, Ey, Ez) and report n_eff\n",
    "fig, ax = plt.subplots(1, 3, figsize=(12, 3))\n",
    "mode_data.plot_field(field_name=\"Ex\", ax=ax[0])\n",
    "ax[0].set_title(\"Ex\")\n",
    "mode_data.plot_field(field_name=\"Ey\", ax=ax[1])\n",
    "ax[1].set_title(\"Ey\")\n",
    "mode_data.plot_field(field_name=\"Ez\", ax=ax[2])\n",
    "ax[2].set_title(\"Ez\")\n",
    "plt.tight_layout()\n",
    "print(\"n_eff:\", float(mode_data.modes.n_eff.isel(f=0, mode_index=0)))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504a8806-1833-4f3b-ac74-0f739e5a1135",
   "metadata": {},
   "source": [
    "## Generation Rate Calculation\n",
    "\n",
    "### Optical Absorption Calculation\n",
    "\n",
    "Now, we will calculate the optical absorption, defined as:\n",
    "\n",
    "$$P_{abs} = \\tfrac{1}{2} \\, \\omega \\, \\varepsilon_0 \\, \\varepsilon'' \\, |E|^2$$\n",
    "\n",
    "We will normalize the power to a scale factor $P_{in}$ of 30 mW, which is a common value for Si photonics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a4cfb31-37fc-42e0-a0f7-49e9e3ed2565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrated power density: 0.0001791073892321918\n"
     ]
    }
   ],
   "source": [
    "# Extract mode fields and scale to target optical power P_in\n",
    "Ex = mode_data.modes.Ex.isel(f=0, mode_index=0)\n",
    "Ey = mode_data.modes.Ey.isel(f=0, mode_index=0)\n",
    "Ez = mode_data.modes.Ez.isel(f=0, mode_index=0)\n",
    "\n",
    "scale = np.sqrt(P_in)\n",
    "Ex_s = Ex * scale\n",
    "Ey_s = Ey * scale\n",
    "Ez_s = Ez * scale\n",
    "\n",
    "# Use xy plane boundaries (fields are not colocated)\n",
    "x = Ex.coords[\"x\"][1:-1]\n",
    "y = Ex.coords[\"y\"][1:-1]\n",
    "\n",
    "# Extract permittivity values\n",
    "eps_xx = mode_data[\"permittivity\"].eps_xx.isel(f=0)\n",
    "eps_yy = mode_data[\"permittivity\"].eps_yy.isel(f=0)\n",
    "eps_zz = mode_data[\"permittivity\"].eps_zz.isel(f=0)\n",
    "\n",
    "# Calculate the squared magnitude for each E-field component.\n",
    "E_squared_magnitude_x = Ex_s * Ex_s.conj()\n",
    "E_squared_magnitude_y = Ey_s * Ey_s.conj()\n",
    "E_squared_magnitude_z = Ez_s * Ez_s.conj()\n",
    "\n",
    "kwargs = {\"fill_value\": 0}\n",
    "Power_density_E_x = np.pi * freq0 * td.EPSILON_0 * eps_xx.imag * E_squared_magnitude_x\n",
    "Power_density_E_y = np.pi * freq0 * td.EPSILON_0 * eps_yy.imag * E_squared_magnitude_y\n",
    "Power_density_E_z = np.pi * freq0 * td.EPSILON_0 * eps_zz.imag * E_squared_magnitude_z\n",
    "Power_density_E = (\n",
    "    Power_density_E_x.interp(x=x, y=y, kwargs=kwargs)\n",
    "    + Power_density_E_y.interp(x=x, y=y, kwargs=kwargs)\n",
    "    + Power_density_E_z.interp(x=x, y=y, kwargs=kwargs)\n",
    ").real\n",
    "\n",
    "\n",
    "print(\"Integrated power density:\", float(Power_density_E.integrate(coord=[\"x\", \"y\"]).item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda709c8",
   "metadata": {},
   "source": [
    "Next, we can calculate the pair generation rate by dividing the power density by the photon energy and the electron charge, assuming a quantum efficiency of 1.\n",
    "\n",
    " $$g = \\tfrac{P_{abs}}{\\hbar\\omega q} \\ \\text{[1/(s µm}^3)]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1bead2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marc/Documents/src/compute/src/Tidy3DCore/_build_local/lib/python3.12/site-packages/xarray/computation/apply_ufunc.py:814: RuntimeWarning: divide by zero encountered in log10\n",
      "  result_data = func(*input_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Photon energy in eV\n",
    "Eph = 2 * np.pi * td.HBAR * td.C_0 / wvl_um\n",
    "\n",
    "# Assuming single optical frequency and a quantum efficiency of 1\n",
    "g = Power_density_E / (Eph * td.Q_e)\n",
    "\n",
    "# Visualize the generation rate\n",
    "ax = np.log10(g.clip(0).T).plot(vmin=0).axes\n",
    "ax.set_aspect(\"equal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e17e98c-9b86-4904-a982-87a763014b99",
   "metadata": {},
   "source": [
    "## Charge Simulation\n",
    "\n",
    "Now, we can create a [HeatChargeSimulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.HeatChargeSimulation.html) object to perform a charge simulation and calculate the generated currents.\n",
    "\n",
    "First, we will run a simulation without applying the previously calculated generation rate in order to obtain the **dark current**, which is the current that flows under a given reverse bias *in the absence of illumination*, originating from thermally generated carriers and tunneling processes.\n",
    "\n",
    "Next, we will simulate the device including the generation rate produced by incident light. This yields the **bright current**, which is the useful photocurrent to be measured. The larger the difference between bright current and dark current (or more precisely, the larger the photocurrent relative to the noise associated with the dark current), the higher the APD sensitivity.\n",
    "\n",
    "\n",
    "First, we define the voltage boundary conditions for the applied bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bcf6a5f-bcbc-497d-abb6-144f5e1dd107",
   "metadata": {},
   "outputs": [],
   "source": [
    "voltages = np.linspace(-11, 0, 12).tolist()\n",
    "voltages = voltages + [0.2, 0.4, 0.6]\n",
    "\n",
    "emitter_bc = td.HeatChargeBoundarySpec(\n",
    "    placement=td.StructureStructureInterface(structures=[emitter.name, si_bottom.name]),\n",
    "    condition=td.VoltageBC(source=td.DCVoltageSource(voltage=0)),\n",
    ")\n",
    "collector_bc = td.HeatChargeBoundarySpec(\n",
    "    placement=td.StructureStructureInterface(structures=[ge_struct.name, collector.name]),\n",
    "    condition=td.VoltageBC(source=td.DCVoltageSource(voltage=voltages)),\n",
    ")\n",
    "bcs = [emitter_bc, collector_bc]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0b10ec-4961-47dd-95c6-2de9f4bce80b",
   "metadata": {},
   "source": [
    "Next, we define the carrier, potential, and steady-state current density monitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff39d0fb-afea-4a0f-95b7-721341b38197",
   "metadata": {},
   "outputs": [],
   "source": [
    "carrier_mnt = td.SteadyFreeCarrierMonitor(\n",
    "    size=(td.inf, td.inf, td.inf), unstructured=True, name=\"carrier_mnt\"\n",
    ")\n",
    "potential_mnt = td.SteadyPotentialMonitor(\n",
    "    size=(td.inf, td.inf, td.inf), unstructured=True, name=\"potential_mnt\"\n",
    ")\n",
    "j_mnt = td.SteadyCurrentDensityMonitor(\n",
    "    size=(td.inf, td.inf, td.inf), unstructured=True, name=\"j_mnt\"\n",
    ")\n",
    "monitors = [carrier_mnt, potential_mnt, j_mnt]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724e5245-a412-4bc1-bb39-9cde56a4d7c0",
   "metadata": {},
   "source": [
    "### Mesh\n",
    "\n",
    "We define a [DistanceUnstructuredGrid](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.DistanceUnstructuredGrid.html) mesh, using [GridRefinementLine](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GridRefinementLine.html) objects to refine the grid at the collector–Ge, Si–Ge, and Si n–Si p interfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dc01d6e-2e5a-4abf-8b52-01a0a7ee51f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "collector_ref = td.GridRefinementLine(\n",
    "    r1=(0, si_b_h + si_t_h + ge_h - 0.02, 0),\n",
    "    r2=(ge_t_w, si_b_h + si_t_h + ge_h - 0.02, 0),\n",
    "    dl_near=si_b_h / 10,\n",
    "    distance_near=0.02 * 2,\n",
    "    distance_bulk=2 * 0.02,\n",
    ")\n",
    "\n",
    "ge_si_ref = td.GridRefinementLine(\n",
    "    r1=(0, si_b_h + si_t_h, 0),\n",
    "    r2=(ge_b_w, si_b_h + si_t_h, 0),\n",
    "    dl_near=si_b_h / 10,\n",
    "    distance_near=0.02 * 2,\n",
    "    distance_bulk=2 * 0.02 * 2,\n",
    ")\n",
    "\n",
    "npp_doping_ref = td.GridRefinementLine(\n",
    "    r1=(0, 0.22, 0),\n",
    "    r2=(si_t_w, 0.22, 0),\n",
    "    dl_near=si_b_h / 10,\n",
    "    distance_near=0.02 * 2,\n",
    "    distance_bulk=2 * 0.02,\n",
    ")\n",
    "\n",
    "mesh_spec = td.DistanceUnstructuredGrid(\n",
    "    # dl_interface=si_b_h / 8 * 2,\n",
    "    dl_interface=si_b_h / 6,\n",
    "    dl_bulk=si_b_h,\n",
    "    distance_interface=si_b_h / 2,\n",
    "    distance_bulk=si_b_h,\n",
    "    relative_min_dl=0,\n",
    "    sampling=500,\n",
    "    uniform_grid_mediums=[Si.name, Ge.name],\n",
    "    mesh_refinements=[collector_ref, ge_si_ref, npp_doping_ref],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c649af1-1381-468d-b6d2-59931635875c",
   "metadata": {},
   "source": [
    "### Tolerances and Analysis Type\n",
    "\n",
    "Next, we define the tolerance settings and set the analysis type to [IsothermalSteadyChargeDCAnalysis](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.IsothermalSteadyChargeDCAnalysis.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79bb9555-d9b2-4c80-b701-7481a36aece8",
   "metadata": {},
   "outputs": [],
   "source": [
    "convergence_settings = td.ChargeToleranceSpec()\n",
    "\n",
    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(\n",
    "    temperature=T0, convergence_dv=0.5, tolerance_settings=convergence_settings, fermi_dirac=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbaffe8-f789-4e65-ab9c-bf8b87113e18",
   "metadata": {},
   "source": [
    "Finally, we can create the [HeatChargeSimulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.HeatChargeSimulation.html) object for the `Scene` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "843db1ed-f0b1-40d5-99d2-2d964e7b50bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = td.HeatChargeSimulation.from_scene(\n",
    "    scene=scene,\n",
    "    size=(si_b_w, total_h, 0),\n",
    "    center=(si_b_w / 2, total_h / 2 - sio2_h / 2, 0),\n",
    "    monitors=monitors,\n",
    "    grid_spec=mesh_spec,\n",
    "    boundary_spec=bcs,\n",
    "    analysis_spec=analysis_type,\n",
    ")\n",
    "\n",
    "sim.plot_property(z=0, property=\"electric_conductivity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd077fa-4155-4a06-9a73-f065cb099b59",
   "metadata": {},
   "source": [
    "### Run Charge Dark Simulation\n",
    "\n",
    "Now, we can run the simulation to calculate the dark current generated without incident light."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76f407f4-5097-4b10-a1f2-bfa45f1e1a4e",
   "metadata": {
    "scrolled": true
   },
   "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\">11:19:17 CEST </span>Loading simulation from local cache. View cached task using web UI\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>at                                                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a-4a43-abf4-f3adb2f62cce'</span></a>.                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:19:17 CEST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI\n",
       "\u001b[2;36m              \u001b[0mat                                                                \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=199791;https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=422815;https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=199791;https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=307659;https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\u001b\\\u001b[32mhec\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=199791;https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\u001b\\\u001b[32m-674530c3-a80\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=199791;https://tidy3d.simulation.cloud/workbench?taskId=hec-674530c3-a80a-4a43-abf4-f3adb2f62cce\u001b\\\u001b[32ma-4a43-abf4-f3adb2f62cce'\u001b[0m\u001b]8;;\u001b\\.                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = web.run(sim, task_name=\"APD_charge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f4d8932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the x component of the electric current density\n",
    "results[j_mnt.name].J.sel(z=0, voltage=-10, method=\"nearest\").sel(axis=0).plot(\n",
    "    grid=False, vmin=-0.015, vmax=0\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "917d110c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the dark current\n",
    "I = abs(results.device_characteristics.steady_dc_current_voltage)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(I.v, I, marker=\"*\")\n",
    "ax.set_ylabel(r\"I (A/$\\mu$m)\")\n",
    "ax.set_xlabel(\"Applied bias (V)\")\n",
    "ax.set_yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8dfbbe-d82b-4259-b379-c906f9555165",
   "metadata": {},
   "source": [
    "### Photocurrent Simulation\n",
    "\n",
    "Next, we update our semiconductor media with a [DistributedGeneration](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.DistributedGeneration.html) object, using <i>from_rate_um3</i>, where we input the calculated generation rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39bb972b-04ce-4493-a8b1-f3dd5787cc72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply generation rate to semiconductor materials\n",
    "charge_structs = []\n",
    "for struct in structures:\n",
    "    if isinstance(struct.medium.charge, td.SemiconductorMedium):\n",
    "        sc = struct.medium\n",
    "        R = list(sc.charge.R)\n",
    "        R.append(td.DistributedGeneration.from_rate_um3(gen_um3=g))\n",
    "        sc = sc.updated_copy(charge=sc.charge.updated_copy(R=R))\n",
    "        charge_structs.append(struct.updated_copy(medium=sc))\n",
    "    else:\n",
    "        charge_structs.append(struct)\n",
    "scene = scene.updated_copy(structures=tuple(charge_structs))\n",
    "\n",
    "sim_with_g = td.HeatChargeSimulation.from_scene(\n",
    "    scene=scene,\n",
    "    size=(si_b_w, total_h, 0),\n",
    "    center=(si_b_w / 2, total_h / 2 - sio2_h / 2, 0),\n",
    "    monitors=monitors,\n",
    "    grid_spec=mesh_spec,\n",
    "    boundary_spec=bcs,\n",
    "    analysis_spec=analysis_type,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e22f06-75bb-4a67-823d-d273c0758908",
   "metadata": {},
   "source": [
    "### Run Bright Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9dba43b2-ccdc-47d2-9f77-46a467566ce9",
   "metadata": {
    "scrolled": true
   },
   "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\">11:19:19 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'APD_charge'</span> with resource_id                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'hec-57389233-61a5-4f85-a416-3f3c99a3a31f'</span> and task_type          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'HEAT_CHARGE'</span>.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:19:19 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'APD_charge'\u001b[0m with resource_id                        \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'hec-57389233-61a5-4f85-a416-3f3c99a3a31f'\u001b[0m and task_type          \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Tidy3D's HeatCharge solver is currently in the beta stage. Cost of\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>HeatCharge simulations is subject to change in the future.        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of\n",
       "\u001b[2;36m              \u001b[0mHeatCharge simulations is subject to change in the future.        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "341896daa6e54694b291db0952c30a76",
       "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\">11:19:25 CEST </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</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;36m11:19:25 CEST\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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\">11:19:27 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:19:27 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7686bb9f656c4ba8849c92ca409f0ac5",
       "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\">11:19:31 CEST </span>Loading results from simulation_data.hdf5                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:19:31 CEST\u001b[0m\u001b[2;36m \u001b[0mLoading results from simulation_data.hdf5                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_with_g = web.run(sim_with_g, task_name=\"APD_charge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bba5897",
   "metadata": {},
   "source": [
    "Finally, we plot the dark and bright currents together. \n",
    "\n",
    "Over the whole bias range, the bright current remains higher than the dark current, and both increase monotonically with reverse bias. Around −2 V we observe the unity-gain operating point, where the onset of impact ionization occurs, consistent with the value reported in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8679e801-53ce-4494-b5f1-caa839a45d17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "I_dark = abs(results.device_characteristics.steady_dc_current_voltage.data)\n",
    "I = abs(results_with_g.device_characteristics.steady_dc_current_voltage.data)\n",
    "V = results.device_characteristics.steady_dc_current_voltage.coords[\"v\"]\n",
    "\n",
    "plt.semilogy(V, I_dark, \"k.-\", label=\"Dark current\")\n",
    "plt.semilogy(V, I, \"r.-\", label=\"Bright current\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Bias (V)\")\n",
    "plt.ylabel(r\"I (A/$\\mu$m)\")  # Label for y-axis\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Active photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to model an on-chip avalanche photodiode using Tidy3D.",
  "feature_image": "./img/APD.png",
  "features": [
   "Charge"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "avalanche photodiode, impact ionization, charge simulation, 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": "3.12.3"
  },
  "title": "How to model an avalanche photodiode using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}