Comparing models for a planar triple-junction solar cell structure - profile calculations¶

This example uses the same structure as the previous, non-profile calculation example; reading through that example first will clarify this one.

We are going to take the same structures, but instead of focusing on the wavelength-dependent absorption in each layer, we’re going to look at the absorption profile in the front surface at only a few wavelengths, again to make sure all the different methods (as listed in the other example) give consistent results.

[1]:

import numpy as np

# solcore imports
from solcore.structure import Layer
from solcore import material

# rayflare imports
from rayflare.textures import planar_surface
from rayflare.structure import Interface, BulkLayer, Structure
from rayflare.matrix_formalism import process_structure, calculate_RAT
from rayflare.options import default_options
from rayflare.transfer_matrix_method import tmm_structure

# plotting imports
import matplotlib.pyplot as plt
import seaborn as sns
from cycler import cycler

import warnings
warnings.filterwarnings('ignore')

pal = sns.cubehelix_palette(10, start=.5, rot=-.9)

cols = cycler('color', pal)

params  = {'axes.prop_cycle': cols}

plt.rcParams.update(params)

[2]:

# Thickness of bottom Ge layer
bulkthick = 300e-6


wavelengths = np.linspace(500, 1000, 7)*1e-9

pal2 = sns.cubehelix_palette(len(wavelengths), start=.5, rot=-.9)

# set options
options = default_options()
options.wavelengths = wavelengths
options.project_name = 'method_comparison_profile'
options.n_rays = 250
options.n_theta_bins = 3
options.lookuptable_angles = 100
options.parallel = True
options.c_azimuth = 0.001

# set up Solcore materials
Ge = material('Ge')()
GaAs = material('GaAs')()
GaInP = material('GaInP')(In=0.5)
Ag = material('Ag')()
SiN = material('Si3N4')()
Air = material('Air')()
Ta2O5 = material('TaOx1')() # Ta2O5 (SOPRA database)
MgF2 = material('MgF2')() # MgF2 (SOPRA database)


front_materials = [Layer(120e-9, MgF2), Layer(74e-9, Ta2O5), Layer(464e-9, GaInP),
                   Layer(1682e-9, GaAs)]
back_materials = [Layer(100E-9, SiN)]

fig2, axes2 = plt.subplots(2, 2, figsize=(9,7))
ax5 = axes2[0,0]
ax6 = axes2[0,1]
ax7 = axes2[1,0]
ax8 = axes2[1,1]

plt.close()

This is so far the same as the previous example, except that we are using fewer wavelengths and we changed the project_name. The key difference in the subsequent cells is that we are going to specify that we want to do a profile calculation (which requires extreme computations, which may be time-consuming, and so are not done by default) by specifying the prof_layers argument in the Interfaces.

[3]:

front_surf = Interface('TMM', layers=front_materials, name = 'GaInP_GaAs_TMM',
                       coherent=True, prof_layers=[1,2,3,4])
back_surf = Interface('TMM', layers=back_materials, name = 'SiN_Ag_TMM',
                      coherent=True, prof_layers=[1])


bulk_Ge = BulkLayer(bulkthick, Ge, name = 'Ge_bulk') # bulk thickness in m

SC = Structure([front_surf, bulk_Ge, back_surf], incidence=Air, transmission=Ag)

process_structure(SC, options)

results_TMM_Matrix = calculate_RAT(SC, options)

profile = results_TMM_Matrix[2]

prof_plot = profile[0]

depths = np.linspace(0, len(prof_plot[0, :]) * options['depth_spacing'] * 1e9,
                     len(prof_plot[0, :]))

for i1 in np.arange(len(wavelengths)):
    ax5.plot(depths, prof_plot[i1, :], color=pal2[i1],
            label=str(round(options['wavelengths'][i1] * 1e9, 1)))

ax5.set_ylabel('Absorbed energy density (nm$^{-1}$)')
ax5.legend(title='Wavelength (nm)')
ax5.set_xlabel('Distance into surface (nm)')
ax5.autoscale(tight=True)

plt.close()

Making matrix for planar surface using TMM for element 0 in structure
Existing angular redistribution matrices found
Existing angular redistribution matrices found
Making matrix for planar surface using TMM for element 2 in structure
Existing angular redistribution matrices found

[4]:

surf = planar_surface() # [texture, flipped texture]

front_surf = Interface('RT_TMM', layers=front_materials, texture=surf,
                       name = 'GaInP_GaAs_RT', coherent=True, prof_layers=[1,2,3,4])
back_surf = Interface('RT_TMM', layers=back_materials, texture = surf,
                      name = 'SiN_Ag_RT_50k', coherent=True, prof_layers=[1])

SC = Structure([front_surf, bulk_Ge, back_surf], incidence=Air, transmission=Ag)

process_structure(SC, options)

results_RT = calculate_RAT(SC, options)

profile = results_RT[2]

prof_plot = profile[0]

depths = np.linspace(0, len(prof_plot[0, :]) * options['depth_spacing'] * 1e9,
                     len(prof_plot[0, :]))

for i1 in np.arange(len(wavelengths)):
    ax6.plot(depths, prof_plot[i1, :], color=pal2[i1],
            label=str(round(options['wavelengths'][i1] * 1e9, 1)))

ax6.set_ylabel('Absorbed energy density (nm$^{-1}$)')
ax6.set_xlabel('Distance into surface (nm)')
ax6.autoscale(tight=True)

plt.close()

Making lookuptable for element 0 in structure
Existing lookup table found
Making lookuptable for element 2 in structure
Existing lookup table found
Ray tracing with TMM lookup table for element 0 in structure
Existing angular redistribution matrices found
Existing angular redistribution matrices found
Ray tracing with TMM lookup table for element 2 in structure
Existing angular redistribution matrices found

[5]:

front_surf = Interface('RCWA', layers=front_materials, name = 'GaInP_GaAs_RCWA',
                       coherent=True, d_vectors = ((500,0), (0,500)), rcwa_orders=2,
                       prof_layers=[1,2,3,4])
back_surf = Interface('RCWA', layers=back_materials, name = 'SiN_Ag_RCWA',
                      coherent=True, d_vectors = ((500,0), (0,500)), rcwa_orders=2,
                      prof_layers=[1])


SC = Structure([front_surf, bulk_Ge, back_surf], incidence=Air, transmission=Ag)

process_structure(SC, options)

results_RCWA_Matrix = calculate_RAT(SC, options)

profile = results_RCWA_Matrix[2]

prof_plot = profile[0]

depths = np.linspace(0, len(prof_plot[0, :]) * options['depth_spacing'] * 1e9,
                     len(prof_plot[0, :]))

for i1 in np.arange(len(wavelengths)):
    ax7.plot(depths, prof_plot[i1, :], color=pal2[i1],
            label=str(round(options['wavelengths'][i1] * 1e9, 1)))

ax7.set_ylabel('Absorbed energy density (nm$^{-1}$)')
ax7.set_xlabel('Distance into surface (nm)')
ax7.autoscale(tight=True)

plt.close()

RCWA calculation for element 0 in structure
Existing angular redistribution matrices found
Existing angular redistribution matrices found
RCWA calculation for element 2 in structure
Existing angular redistribution matrices found

[6]:

all_layers = front_materials + [Layer(bulkthick, Ge)] + back_materials

coh_list = len(front_materials)*['c'] + ['i'] + ['c']
options.coherency_list = coh_list
options.coherent = False

OS_layers = tmm_structure(all_layers, incidence=Air, transmission=Ag, no_back_reflection=False)

TMM_res = OS_layers.calculate(options, profile=[1,2,3,4,5, 6])

for i1 in np.arange(len(wavelengths)):
    ax8.plot(depths, TMM_res['profile'][i1, :len(depths)], color=pal2[i1],
            label=str(round(options['wavelengths'][i1] * 1e9, 1)))

ax8.set_ylabel('Absorbed energy density (nm$^{-1}$)')
ax8.set_xlabel('Distance into surface (nm)')

ax8.autoscale(tight=True)

fig2.tight_layout()
fig2

[6]:

RayFlare

Documentation

Comparing models for a planar triple-junction solar cell structure - profile calculations

Comparing models for a planar triple-junction solar cell structure - profile calculations¶