{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Calculation of absorptivity/emissivity of a silicon heterojunction cell with pyramid textures\n", "This example calculates the absorption (and thus emissivity) in all the individual layers of a silicon heterojunction (HIT) solar cell. [This paper](https://doi.org/10.1016/j.solmat.2019.110051) gives more details.\n", "\n", "Start with relevant imports:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { "end_time": "2024-09-21T17:50:26.743912Z", "start_time": "2024-09-21T17:50:26.732949Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "\n", "from solcore.structure import Layer\n", "from solcore import material\n", "from solcore.light_source import LightSource\n", "from solcore.constants import q\n", "\n", "from rayflare.textures import regular_pyramids\n", "from rayflare.structure import Interface, BulkLayer, Structure\n", "from rayflare.matrix_formalism import calculate_RAT\n", "from rayflare.matrix_formalism import process_structure\n", "from rayflare.options import default_options\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from cycler import cycler" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We then generate the wavelengths (equally spaced between 300 nm and 16 microns on a log scale) at which to calculate, and set some options. Note that for this example, we are using relatively few rays and angular bins so the example runs in a reasonable amount of time, but this means the results will have a lot of noise due to the stochastic nature of the ray-tracing. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { "end_time": "2024-09-21T17:50:26.744931Z", "start_time": "2024-09-21T17:50:26.737057Z" } }, "outputs": [], "source": [ "wavelengths = np.linspace(np.log(300), np.log(16*1000), 104)\n", "wavelengths = np.round(np.floor(np.exp(wavelengths))*1e-9, 12)\n", "\n", "options = default_options()\n", "options.wavelength = wavelengths\n", "options.project_name = 'HIT_notebook'\n", "options.n_rays = 10000\n", "options.n_theta_bins = 20\n", "options.nx = 5\n", "options.ny = 5" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Define the materials and the front and back material stacks. These are the layers which are deposited on the surface texture of the Si on each side. Note that these are custom materials (run the non-notebook version of this example in the examples folder to add them to your database)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { "end_time": "2024-09-21T17:50:26.749555Z", "start_time": "2024-09-21T17:50:26.741317Z" } }, "outputs": [], "source": [ "Si = material('Si_UVtoMIR')()\n", "Air = material('Air')()\n", "ITO_front = material('ITO_front')()\n", "ITO_back = material('ITO_back')()\n", "Ag = material('Ag_Jiang')()\n", "aSi_i = material('aSi_i')()\n", "aSi_p = material('aSi_p')()\n", "aSi_n = material('aSi_n')()\n", "\n", "\n", "# stack based on doi:10.1038/s41563-018-0115-4\n", "front_materials = [Layer(80e-9, ITO_front), Layer(6.5e-9, aSi_p), Layer(6.5e-9, aSi_i)]\n", "back_materials = [Layer(6.5e-9, aSi_i), Layer(6.5e-9, aSi_p), Layer(240e-9, ITO_back)]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Define the surface textures for the front and back surface. These are pyramids in both cases, but because the pyramids are achieved using a chemical etch and are thus 'pointing out' of the surface in both cases. RayFlare defines 'upright' relative to the direction from which light is coming in, so the pyramids on the front surface are upright while those on the back surface are inverted (upright=False). The surface textures are used in combination with the layer stacks defined above to define the Interfaces of the cell, which are to be treated with the 'RT_TMM' method (ray-tracing + transfer-matrix method to correctly treat the surface layers). Then, finally, the bulk of the cell (Si) and thus the overall structure (front surface, bulk, back surface) is defined, also specifying the incidence medium above the cell (air) and the semi-infinite transmission medium behind the cell (silver)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { "end_time": "2024-09-21T17:50:26.758004Z", "start_time": "2024-09-21T17:50:26.747699Z" } }, "outputs": [], "source": [ "surf = regular_pyramids(elevation_angle=55, upright=True)\n", "surf_back = regular_pyramids(elevation_angle=55, upright=False)\n", "\n", "front_surf = Interface('RT_TMM', texture=surf, layers=front_materials, name='HIT_front',\n", " coherent=True)\n", "back_surf = Interface('RT_TMM', texture=surf_back, layers=back_materials, name='HIT_back',\n", " coherent=True)\n", "\n", "\n", "bulk_Si = BulkLayer(170e-6, Si, name = 'Si_bulk') # bulk thickness in m\n", "\n", "SC = Structure([front_surf, bulk_Si, back_surf], incidence=Air, transmission=Ag)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we are finished setting up, we run the calculation. process_structure calculates all the relevant matrices for the front and back surfaces (this can take a while). Then calculate_RAT does the matrix multiplication (this is fast). We then extract relevant results and use them to calculate the predicted photogenerated current in the cell." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { "end_time": "2024-09-21T17:51:37.309212Z", "start_time": "2024-09-21T17:50:26.752520Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Making RT/TMM lookuptable for element 0 in structure\n", "INFO: Making RT/TMM lookuptable for element 2 in structure\n", "INFO: Ray tracing with TMM lookup table for element 0 in structure\n", "INFO: Calculating matrix only for incidence theta/phi\n", "INFO: RT calculation for wavelength = 299.0 nm\n", "INFO: RT calculation for wavelength = 311.0 nm\n", "INFO: RT calculation for wavelength = 324.0 nm\n", "INFO: RT calculation for wavelength = 336.0 nm\n", "INFO: RT calculation for wavelength = 350.0 nm\n", "INFO: RT calculation for wavelength = 363.0 nm\n", "INFO: RT calculation for wavelength = 378.0 nm\n", "INFO: RT calculation for wavelength = 393.0 nm\n", "INFO: RT calculation for wavelength = 408.0 nm\n", "INFO: RT calculation for wavelength = 424.0 nm\n", "INFO: RT calculation for wavelength = 441.0 nm\n", "INFO: RT calculation for wavelength = 458.0 nm\n", "INFO: RT calculation for wavelength = 476.0 nm\n", "INFO: RT calculation for wavelength = 495.0 nm\n", "INFO: RT calculation for wavelength = 515.0 nm\n", "INFO: RT calculation for wavelength = 535.0 nm\n", "INFO: RT calculation for wavelength = 556.0 nm\n", "INFO: RT calculation for wavelength = 578.0 nm\n", "INFO: RT calculation for wavelength = 601.0 nm\n", "INFO: RT calculation for wavelength = 624.0 nm\n", "INFO: RT calculation for wavelength = 701.0 nm\n", "INFO: RT calculation for wavelength = 649.0 nm\n", "INFO: RT calculation for wavelength = 818.0 nm\n", "INFO: RT calculation for wavelength = 757.0 nm\n", "INFO: RT calculation for wavelength = 884.0 nm\n", "INFO: RT calculation for wavelength = 955.0 nm\n", "INFO: RT calculation for wavelength = 1031.0 nm\n", "INFO: RT calculation for wavelength = 1114.0 nm\n", "INFO: RT calculation for wavelength = 1204.0 nm\n", "INFO: RT calculation for wavelength = 850.0 nm\n", "INFO: RT calculation for wavelength = 674.0 nm\n", "INFO: RT calculation for wavelength = 919.0 nm\n", "INFO: RT calculation for wavelength = 1300.0 nm\n", "INFO: RT calculation for wavelength = 1072.0 nm\n", "INFO: RT calculation for wavelength = 729.0 nm\n", "INFO: RT calculation for wavelength = 787.0 nm\n", "INFO: RT calculation for wavelength = 1251.0 nm\n", "INFO: RT calculation for wavelength = 992.0 nm\n", "INFO: RT calculation for wavelength = 1158.0 nm\n", "INFO: RT calculation for wavelength = 1405.0 nm\n", "INFO: RT calculation for wavelength = 1518.0 nm\n", "INFO: RT calculation for wavelength = 1640.0 nm\n", "INFO: RT calculation for wavelength = 1771.0 nm\n", "INFO: RT calculation for wavelength = 1913.0000000000002 nm\n", "INFO: RT calculation for wavelength = 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wavelength = 4651.0 nm\n", "INFO: RT calculation for wavelength = 4834.0 nm\n", "INFO: RT calculation for wavelength = 5024.0 nm\n", "INFO: RT calculation for wavelength = 5222.0 nm\n", "INFO: RT calculation for wavelength = 5428.0 nm\n", "INFO: RT calculation for wavelength = 5641.0 nm\n", "INFO: RT calculation for wavelength = 5863.0 nm\n", "INFO: RT calculation for wavelength = 6094.0 nm\n", "INFO: RT calculation for wavelength = 6334.0 nm\n", "INFO: RT calculation for wavelength = 6583.0 nm\n", "INFO: RT calculation for wavelength = 6842.0 nm\n", "INFO: RT calculation for wavelength = 7112.0 nm\n", "INFO: RT calculation for wavelength = 7392.0 nm\n", "INFO: RT calculation for wavelength = 7683.000000000001 nm\n", "INFO: RT calculation for wavelength = 7985.000000000001 nm\n", "INFO: RT calculation for wavelength = 8300.0 nm\n", "INFO: RT calculation for wavelength = 8626.0 nm\n", "INFO: RT calculation for wavelength = 8966.0 nm\n", "INFO: RT calculation for wavelength = 9319.0 nm\n", "INFO: RT calculation for wavelength = 9686.0 nm\n", "INFO: RT calculation for wavelength = 10067.0 nm\n", "INFO: RT calculation for wavelength = 10463.0 nm\n", "INFO: RT calculation for wavelength = 10875.0 nm\n", "INFO: RT calculation for wavelength = 11303.0 nm\n", "INFO: RT calculation for wavelength = 11748.0 nm\n", "INFO: RT calculation for wavelength = 12210.0 nm\n", "INFO: RT calculation for wavelength = 12691.0 nm\n", "INFO: RT calculation for wavelength = 13191.0 nm\n", "INFO: RT calculation for wavelength = 13710.0 nm\n", "INFO: RT calculation for wavelength = 14250.0 nm\n", "INFO: RT calculation for wavelength = 14811.0 nm\n", "INFO: RT calculation for wavelength = 15393.999999999998 nm\n", "INFO: RT calculation for wavelength = 16000.0 nm\n", "INFO: Ray tracing with TMM lookup table for element 2 in structure\n", "INFO: RT calculation for wavelength = 299.0 nm\n", "INFO: RT calculation for wavelength = 311.0 nm\n", "INFO: RT calculation for wavelength = 324.0 nm\n", "INFO: RT calculation for wavelength = 336.0 nm\n", "INFO: RT calculation for wavelength = 350.0 nm\n", "INFO: RT calculation for wavelength = 363.0 nm\n", "INFO: RT calculation for wavelength = 378.0 nm\n", "INFO: RT calculation for wavelength = 424.0 nm\n", "INFO: RT calculation for wavelength = 393.0 nm\n", "INFO: RT calculation for wavelength = 408.0 nm\n", "INFO: RT calculation for wavelength = 441.0 nm\n", "INFO: RT calculation for wavelength = 458.0 nm\n", "INFO: RT calculation for wavelength = 476.0 nm\n", "INFO: RT calculation for wavelength = 495.0 nm\n", "INFO: RT calculation for wavelength = 515.0 nm\n", "INFO: RT calculation for wavelength = 535.0 nm\n", "INFO: RT calculation for wavelength = 556.0 nm\n", "INFO: RT calculation for wavelength = 578.0 nm\n", "INFO: RT calculation for wavelength = 601.0 nm\n", "INFO: RT calculation for wavelength = 624.0 nm\n", "INFO: RT calculation for wavelength = 649.0 nm\n", "INFO: RT calculation for wavelength = 674.0 nm\n", "INFO: RT calculation for wavelength = 701.0 nm\n", "INFO: RT calculation for wavelength = 729.0 nm\n", "INFO: RT calculation for wavelength = 757.0 nm\n", "INFO: RT calculation for wavelength = 787.0 nm\n", "INFO: RT calculation for wavelength = 818.0 nm\n", "INFO: RT calculation for wavelength = 850.0 nm\n", "INFO: RT calculation for wavelength = 884.0 nm\n", "INFO: RT calculation for wavelength = 919.0 nm\n", "INFO: RT calculation for wavelength = 955.0 nm\n", "INFO: RT calculation for wavelength = 992.0 nm\n", "INFO: RT calculation for wavelength = 1031.0 nm\n", "INFO: RT calculation for wavelength = 1072.0 nm\n", "INFO: RT calculation for wavelength = 1114.0 nm\n", "INFO: RT calculation for wavelength = 1158.0 nm\n", "INFO: RT calculation for wavelength = 1204.0 nm\n", "INFO: RT calculation for wavelength = 1251.0 nm\n", "INFO: RT calculation for wavelength = 1300.0 nm\n", "INFO: RT calculation for wavelength = 1352.0 nm\n", "INFO: RT calculation for wavelength = 1405.0 nm\n", "INFO: RT calculation for wavelength = 1460.0 nm\n", "INFO: RT calculation for wavelength = 1518.0 nm\n", "INFO: RT calculation for wavelength = 1577.0 nm\n", "INFO: RT calculation for wavelength = 1640.0 nm\n", "INFO: RT calculation for wavelength = 1704.0 nm\n", "INFO: RT calculation for wavelength = 1771.0 nm\n", "INFO: RT calculation for wavelength = 1841.0 nm\n", "INFO: RT calculation for wavelength = 1913.0000000000002 nm\n", "INFO: RT calculation for wavelength = 1989.0 nm\n", "INFO: RT calculation for wavelength = 2067.0 nm\n", "INFO: RT calculation for wavelength = 2149.0 nm\n", "INFO: RT calculation for wavelength = 2233.0 nm\n", "INFO: RT calculation for wavelength = 2321.0 nm\n", "INFO: RT calculation for wavelength = 2412.0 nm\n", "INFO: RT calculation for wavelength = 2507.0 nm\n", "INFO: RT calculation for wavelength = 2606.0 nm\n", "INFO: RT calculation for wavelength = 2709.0 nm\n", "INFO: RT calculation for wavelength = 2815.0 nm\n", "INFO: RT calculation for wavelength = 2926.0 nm\n", "INFO: RT calculation for wavelength = 3161.0 nm\n", "INFO: RT calculation for wavelength = 3041.0 nm\n", "INFO: RT calculation for wavelength = 3286.0 nm\n", "INFO: RT calculation for wavelength = 3415.0 nm\n", "INFO: RT calculation for wavelength = 3549.0 nm\n", "INFO: RT calculation for wavelength = 3689.0 nm\n", "INFO: RT calculation for wavelength = 3834.0 nm\n", "INFO: RT calculation for wavelength = 3985.0 nm\n", "INFO: RT calculation for wavelength = 4142.0 nm\n", "INFO: RT calculation for wavelength = 4305.0 nm\n", "INFO: RT calculation for wavelength = 4475.0 nm\n", "INFO: RT calculation for wavelength = 4651.0 nm\n", "INFO: RT calculation for wavelength = 4834.0 nm\n", "INFO: RT calculation for wavelength = 5024.0 nm\n", "INFO: RT calculation for wavelength = 5222.0 nm\n", "INFO: RT calculation for wavelength = 5428.0 nm\n", "INFO: RT calculation for wavelength = 5641.0 nm\n", "INFO: RT calculation for wavelength = 5863.0 nm\n", "INFO: RT calculation for wavelength = 6094.0 nm\n", "INFO: RT calculation for wavelength = 6334.0 nm\n", "INFO: RT calculation for wavelength = 6583.0 nm\n", "INFO: RT calculation for wavelength = 6842.0 nm\n", "INFO: RT calculation for wavelength = 7112.0 nm\n", "INFO: RT calculation for wavelength = 7392.0 nm\n", "INFO: RT calculation for wavelength = 7985.000000000001 nm\n", "INFO: RT calculation for wavelength = 7683.000000000001 nm\n", "INFO: RT calculation for wavelength = 8300.0 nm\n", "INFO: RT calculation for wavelength = 8626.0 nm\n", "INFO: RT calculation for wavelength = 8966.0 nm\n", "INFO: RT calculation for wavelength = 9319.0 nm\n", "INFO: RT calculation for wavelength = 9686.0 nm\n", "INFO: RT calculation for wavelength = 10067.0 nm\n", "INFO: RT calculation for wavelength = 10463.0 nm\n", "INFO: RT calculation for wavelength = 10875.0 nm\n", "INFO: RT calculation for wavelength = 11303.0 nm\n", "INFO: RT calculation for wavelength = 11748.0 nm\n", "INFO: RT calculation for wavelength = 12210.0 nm\n", "INFO: RT calculation for wavelength = 12691.0 nm\n", "INFO: RT calculation for wavelength = 13191.0 nm\n", "INFO: RT calculation for wavelength = 13710.0 nm\n", "INFO: RT calculation for wavelength = 14250.0 nm\n", "INFO: RT calculation for wavelength = 14811.0 nm\n", "INFO: RT calculation for wavelength = 15393.999999999998 nm\n", "INFO: RT calculation for wavelength = 16000.0 nm\n", "INFO: After iteration 1: maximum power fraction remaining = 0.6263919217313435\n", "INFO: After iteration 2: maximum power fraction remaining = 0.38956713166279705\n", "INFO: After iteration 3: maximum power fraction remaining = 0.24515482469704067\n", "INFO: After iteration 4: maximum power fraction remaining = 0.15418570734748918\n", "INFO: After iteration 5: maximum power fraction remaining = 0.09678801368316606\n", "INFO: After iteration 6: maximum power fraction remaining = 0.06066147968810933\n", "INFO: After iteration 7: maximum power fraction remaining = 0.03797784796825294\n", "INFO: After iteration 8: maximum power fraction remaining = 0.023759689844090878\n", "INFO: After iteration 9: maximum power fraction remaining = 0.014857958555146896\n", "INFO: After iteration 10: maximum power fraction remaining = 0.009367916110272037\n" ] } ], "source": [ "process_structure(SC, options)\n", "\n", "results = calculate_RAT(SC, options)\n", "\n", "RAT = results[0]\n", "results_per_pass = results[1]\n", "\n", "\n", "R_per_pass = np.sum(results_per_pass['r'][0], 2)\n", "R_0 = R_per_pass[0]\n", "R_escape = np.sum(R_per_pass[1:, :], 0)\n", "\n", "# only select absorbing layers, sum over passes\n", "results_per_layer_front = np.sum(results_per_pass['a'][0], 0)\n", "\n", "results_per_layer_back = np.sum(results_per_pass['a'][1], 0)\n", "\n", "\n", "allres = np.flip(np.stack((results_per_layer_front[:,0],\n", " results_per_layer_front[:,1]+results_per_layer_front[:,2],\n", " RAT['A_bulk'][0, :],\n", " results_per_layer_back[:,0] + results_per_layer_back[:,1],\n", " results_per_layer_back[:,2], RAT['T'][0, :])), 0)\n", "\n", "# calculated photogenerated current (Jsc with 100% EQE)\n", "\n", "spectr_flux = LightSource(source_type='standard', version='AM1.5g',\n", " x=wavelengths, output_units='photon_flux_per_m',\n", " concentration=1).spectrum(wavelengths)[1]\n", "\n", "Jph_Si = q * np.trapz(RAT['A_bulk'][0] * spectr_flux, wavelengths)/10 # mA/cm2" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Finally, plot the results:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" }, "ExecuteTime": { "end_time": "2024-09-21T17:51:37.458552Z", "start_time": "2024-09-21T17:51:37.318643Z" } }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pal = sns.cubehelix_palette(allres.shape[0], start=.5, rot=-.9)\n", "pal.reverse()\n", "cols = cycler('color', pal)\n", "\n", "params = {'legend.fontsize': 'small',\n", " 'axes.labelsize': 'small',\n", " 'axes.titlesize': 'small',\n", " 'xtick.labelsize': 'small',\n", " 'ytick.labelsize': 'small',\n", " 'axes.prop_cycle': cols}\n", "\n", "plt.rcParams.update(params)\n", "\n", "# plot total R, A, T\n", "fig = plt.figure(figsize=(5,4))\n", "ax = plt.subplot(111)\n", "ax.semilogx(options['wavelength']*1e6, R_escape + R_0, '--k', label=r'$R_{total}$')\n", "ax.semilogx(options['wavelength']*1e6, R_0, '-.k', label=r'$R_0$')\n", "ax.stackplot(options['wavelength']*1e6, allres,\n", " labels=['Ag', 'Back ITO', 'a-Si (back)', 'Bulk Si', 'a-Si (front)', 'Front ITO'])\n", "ax.set_xlabel(r'Wavelength ($\\mu$m)')\n", "ax.set_ylabel('Absorption/Emissivity')\n", "ax.set_xlim(min(options['wavelength']*1e6), max(options['wavelength']*1e6))\n", "ax.set_ylim(0, 1)\n", "plt.legend()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }