{ "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", "\"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-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,\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=9.1e16, B=4.9e6, 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_Lossless\"],\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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", 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" ] }, "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": [ "
" ] }, "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": [ "
13:04:32 UTC Created task 'APD_opt' with resource_id                            \n",
       "             'mos-36096a50-953c-48d9-b621-c49ed76512ce' and task_type 'MODE'.   \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m13:04:32 UTC\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-36096a50-953c-48d9-b621-c49ed76512ce'\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": "87dd762a2a434e82b021dabe060b65ff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:04:35 UTC Estimated FlexCredit cost: 0.009. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m13:04:35 UTC\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.009\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": [
       "
13:04:37 UTC status = success                                                   \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m13:04:37 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7aa5dd1a74c34af08cbd41d9682fd673",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:04:47 UTC Loading simulation from simulation_data.hdf5                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:04:47 UTC\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: 4.338929562235534\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "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: 0.00017910777968293556\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/flexdaemon/flexcompute/tidy3d-A/.venv/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": [
       "
"
      ]
     },
     "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",
    "\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",
    "    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": [
       "
"
      ]
     },
     "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": [
       "
13:04:50 UTC Created task 'APD_charge' with resource_id                         \n",
       "             'hec-541f23e8-7331-4fd0-b156-bcac1300e50f' and task_type           \n",
       "             'HEAT_CHARGE'.                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:04:50 UTC\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-541f23e8-7331-4fd0-b156-bcac1300e50f'\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": [
       "
             Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
       "             HeatCharge simulations is subject to change in the future.         \n",
       "
\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": "c658e89c26fd469f92cd7c55fcd68a98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:04:53 UTC Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:04:53 UTC\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": [
       "
13:04:55 UTC status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:04:55 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efb60a75da2647df95791e86d1c2d584",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:05:02 UTC Loading simulation from simulation_data.hdf5                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:05:02 UTC\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": [
       "
"
      ]
     },
     "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": [
       "
"
      ]
     },
     "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 from_rate_um3, 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": [
       "
13:05:03 UTC Created task 'APD_charge' with resource_id                         \n",
       "             'hec-22fc96d5-772b-40b4-a264-9b306bc32498' and task_type           \n",
       "             'HEAT_CHARGE'.                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:05:03 UTC\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-22fc96d5-772b-40b4-a264-9b306bc32498'\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": [
       "
             Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
       "             HeatCharge simulations is subject to change in the future.         \n",
       "
\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": "6b01fff9f3194fba92801b935089f2be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:05:08 UTC Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:05:08 UTC\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": [
       "
13:05:10 UTC status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:05:10 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
       "             Terminating the Python script will not stop the job running on the \n",
       "             cloud.                                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:05:20 UTC status = preprocess                                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:05:20 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:14:39 UTC starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:14:39 UTC\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:35:57 UTC status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:57 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2428a367028e4eab9bfef522e18b106f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
13:36:03 UTC Loading simulation from simulation_data.hdf5                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:03 UTC\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 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": [
       "
"
      ]
     },
     "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95a6e605-1d3b-4777-b49c-a0e22350b5ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.2"
  },
  "title": "How to model an avalanche photodiode using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}