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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3612181f-629c-481d-922b-65cd8fb18a56",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "781520ab",
+   "metadata": {},
+   "source": [
+    "# Avalanche Photodiode\n",
+    "\n",
+    "This notebook demonstrates the modeling of an on-chip avalanche photodiode (APD).\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",
+    " and then compute the electron–hole pair generation rate:\n",
+    "\n",
+    " $g = \\tfrac{P_{abs}}{h\\nu q} \\ \\text{[1/(s µm}^3)]$\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 = 0.15 # height\n",
+    "si_b_h = 0.35 / 5  # height\n",
+    "\n",
+    "# si_top\n",
+    "si_t_w = 2.2  # width\n",
+    "# si_t_h = 0.22 + 0.1 # height\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 = 300\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-12, alpha_T=0, A=1, B=0, C=1, N0=7.1e15, alpha=1\n",
+    "            ),\n",
+    "            tau_p=td.FossumCarrierLifetime(\n",
+    "                tau_300=1e-12, 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=1.9e6, sigma=2.5),\n",
+    "        td.SelberherrImpactIonization(\n",
+    "            # alpha_n_inf=7.03e5/8000,\n",
+    "            # alpha_p_inf=1.582e6/8000,\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,  # 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=9.1e16, B=4.9e6, E_0=1, sigma=2.5),\n",
+    "        td.SelberherrImpactIonization(\n",
+    "            # alpha_n_inf=1.55e7/8000, # NOTE: this is has been modified (it doesn't fit the reference)\n",
+    "            # alpha_p_inf=2.15e5/8000,\n",
+    "            alpha_n_inf=4.9e5,\n",
+    "            alpha_p_inf=2.15e5,\n",
+    "            # E_n_crit=1556980, # NOTE: this is has been modified (it doesn't fit the reference)\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=0.02,\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",
+    "# p_Ge = td.ConstantDoping.from_bounds(concentration=1e18, rmin=(-ge_b_w, si_b_h+si_t_h-s_ol, -z_size), rmax=(ge_b_w, si_b_h+si_t_h+ge_h+s_ol, z_size))\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=1e20,\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_Lossless\"],\n",
+    "    # optical=td.Medium(permittivity=3.4777**2)\n",
+    "    name=\"Si\",\n",
+    ")\n",
+    "\n",
+    "Ge = td.MultiPhysicsMedium(\n",
+    "    charge=doped_Ge,\n",
+    "    # optical=td.material_library[\"Ge\"][\"Palik_Lossy\"],\n",
+    "    optical=td.material_library[\"Ge\"][\"Nunley\"],\n",
+    "    # optical=td.Medium(permittivity=4.2162**2)\n",
+    "    name=\"Ge\",\n",
+    ")\n",
+    "\n",
+    "SiO2 = td.MultiPhysicsMedium(\n",
+    "    charge=td.ChargeInsulatorMedium(permittivity=3.9),\n",
+    "    # optical=td.material_library[\"SiO2\"][\"Palik_Lossy\"],\n",
+    "    optical=td.material_library[\"SiO2\"][\"Horiba\"],\n",
+    "    # optical=td.Medium(permittivity=1.44**2)\n",
+    "    name=\"SiO2\",\n",
+    ")\n",
+    "\n",
+    "Al = td.MultiPhysicsMedium(\n",
+    "    charge=td.ChargeConductorMedium(conductivity=1),\n",
+    "    optical=td.material_library[\"Al\"][\"RakicLorentzDrude1998\"],\n",
+    "    # optical=td.Medium(permittivity=1.5785**2)\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\">19:37:08 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'APD_opt'</span> with resource_id                            \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mos-d858609f-e725-4de4-83be-46f776b4c4f2'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'MODE'</span>.   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:37:08 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'APD_opt'\u001b[0m with resource_id                            \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'mos-d858609f-e725-4de4-83be-46f776b4c4f2'\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": "6554d8dd225a4eb3833e8c03c7cd9f41",
+       "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\">19:37:19 -03 </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007</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;36m19:37:19 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.007\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\">19:37:22 -03 </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:37:22 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4cefb9922ea4d0ba86824430683f6c8",
+       "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\">19:37:39 -03 </span>Loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:37:39 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \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: 4.338929562235545\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",
+    "$$P_{abs} = \\tfrac{1}{2} \\, \\omega \\, \\varepsilon_0 \\, \\varepsilon'' \\, |E|^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8a4cfb31-37fc-42e0-a0f7-49e9e3ed2565",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Integrated power density: 0.0001791077796829375\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\"])))"
+   ]
+  },
+  {
+   "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",
+    "$$g = \\tfrac{P_{abs}}{h\\nu q} \\ \\text{[1/(s µm}^3)]$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1bead2eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/filipe/anaconda3/envs/develop/lib/python3.11/site-packages/xarray/computation/apply_ufunc.py:818: 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 current, both without illumination (dark current) and with incident light (bright current).\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",
+    "\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=0.02 / 5,\n",
+    "    distance_near=0.02,\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=0.02 / 5,\n",
+    "    distance_near=0.02,\n",
+    "    distance_bulk=2 * 0.02,\n",
+    ")\n",
+    "si_b_h + 0.07\n",
+    "\n",
+    "npp_doping_ref = td.GridRefinementLine(\n",
+    "    r1=(0, 0.22, 0),\n",
+    "    r2=(si_t_w, 0.22, 0),\n",
+    "    dl_near=0.02 / 5,\n",
+    "    distance_near=0.02,\n",
+    "    distance_bulk=2 * 0.02,\n",
+    ")\n",
+    "\n",
+    "mesh_spec = td.DistanceUnstructuredGrid(\n",
+    "    dl_interface=si_b_h / 8,\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",
+    "    # non_refined_structures=[],\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",
+    "    rel_tol=1e2, abs_tol=1e12, max_iters=100, ramp_up_iters=2\n",
+    ")\n",
+    "\n",
+    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(\n",
+    "    temperature=T0, convergence_dv=12, 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": {
+      "image/png": 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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\">19:37:42 -03 </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-88781298-fb43-4025-8ceb-db886d8c1aba'</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;36m19:37:42 -03\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-88781298-fb43-4025-8ceb-db886d8c1aba'\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": "97976840c29946ad881ed7af06cfa3d4",
+       "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\">19:37:53 -03 </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;36m19:37:53 -03\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\">19:37:55 -03 </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:37:55 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3ce484689b9401b9baf6b5570d56462",
+       "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\">19:38:22 -03 </span>Loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:38:22 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from simulation_data.hdf5                       \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 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_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=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\">19:38:24 -03 </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-384c0b2d-171c-4967-958a-3c41adff1f68'</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;36m19:38:24 -03\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-384c0b2d-171c-4967-958a-3c41adff1f68'\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": "cd24a7d26f7642e6b22bff18d1b273a6",
+       "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\">19:38:47 -03 </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;36m19:38:47 -03\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\">19:38:49 -03 </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:38:49 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acf030cfda0048259acf07b93170e06f",
+       "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\">19:38:58 -03 </span>Loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:38:58 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation 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 can plot together the dark and bright currents."
+   ]
+  },
+  {
+   "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": "develop",
+   "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": "3.11.14"
+  },
+  "title": "How to model an avalanche photodiode using Tidy3D | Flexcompute"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/case_studies/pic_active.rst b/docs/case_studies/pic_active.rst
index 601b1663..607a6beb 100644
--- a/docs/case_studies/pic_active.rst
+++ b/docs/case_studies/pic_active.rst
@@ -17,3 +17,4 @@ At the moment, Tidy3D’s heat solver can be used with the FDTD solver to model
     ../../CPOHeat
     ../../PhotoThermalWaveguides
     ../../PINMachZehnder
+    ../../AvalanchePhotodiode
\ No newline at end of file
diff --git a/img/APD.png b/img/APD.png
new file mode 100644
index 00000000..8d382aeb
Binary files /dev/null and b/img/APD.png differ