examples/notebooks/Quasi3D_3J_solar_Cell.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from solcore.structure import Junction\n",
    "from solcore.solar_cell import SolarCell\n",
    "from solcore.light_source import LightSource\n",
    "from solcore.spice import solve_quasi_3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load the masks defining the illumination pattern and the contacts. Both must be greyscale images<br>\n",
    "The solver expect images with values between 0 and 255 and imread of a PNG image is between 0 and 1, even when<br>\n",
    "it is in grey, so we scale it multiplying by 255. If the image were JPG, the result would be already in (0,255)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "illuminationMask = (plt.imread('../data/masks_illumination.png') * 255).astype(np.int)\n",
    "contactsMask = (plt.imread('../data/masks_sq.png') * 255).astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx, ny = illuminationMask.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For symmetry arguments (not completely true for the illumination), we can mode just 1/4 of the device and then<br>\n",
    "multiply the current by 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "illuminationMask = illuminationMask[int(nx / 2):, int(ny / 2):]\n",
    "contactsMask = contactsMask[int(nx / 2):, int(ny / 2):]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Size of the pixels (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lx = 10e-6\n",
    "Ly = 10e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Height of the metal fingers (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = 2.2e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contact resistance (Ohm m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rcontact = 3e-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resistivity metal fingers (Ohm m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rline = 2e-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bias (V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vini = 0\n",
    "vfin = 1.3\n",
    "step = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 298\n",
    "db_junction = Junction(kind='2D', T=T, reff=1, jref=300, Eg=0.66, A=1, R_sheet_top=100, R_sheet_bot=1e-16,\n",
    "                       R_shunt=1e16, n=3.5)\n",
    "db_junction2 = Junction(kind='2D', T=T, reff=1, jref=300, Eg=1.4, A=1, R_sheet_top=100, R_sheet_bot=1e-16,\n",
    "                        R_shunt=1e16, n=3.5)\n",
    "db_junction3 = Junction(kind='2D', T=T, reff=0.5, jref=300, Eg=1.8, A=1, R_sheet_top=100, R_sheet_bot=100,\n",
    "                        R_shunt=1e16, n=3.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a single junction, this will have >28800 nodes and for the full 3J it will be >86400, so it is worth to<br>\n",
    "exploit symmetries whenever possible. A smaller number of nodes also makes the solver more robust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_solar_cell = SolarCell([db_junction2], T=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wl = np.linspace(350, 2000, 301) * 1e-9\n",
    "light_source = LightSource(source_type='standard', version='AM1.5g', x=wl, output_units='photon_flux_per_m',\n",
    "                           concentration=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {'light_iv': True, 'wavelength': wl, 'light_source': light_source, 'optics_method': 'BL'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "V, I, Vall, Vmet = solve_quasi_3D(my_solar_cell, illuminationMask, contactsMask, options=options, Lx=Lx, Ly=Ly, h=h,\n",
    "                                  R_back=1e-16, R_contact=Rcontact, R_line=Rline, bias_start=vini, bias_end=vfin,\n",
    "                                  bias_step=step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we model 1/4 of the device, we multiply the current by 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = I * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1)\n",
    "plt.imshow(Vall[:, :, -2, -1])\n",
    "\n",
    "plt.figure(2)\n",
    "plt.semilogy(V, abs(I))\n",
    "plt.show()"
   ]
  }
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