options = default_options
options.nm_spacing = 0.5
options.wavelengths = wavelengths
options.project_name = 'test_matrix2'
options.n_rays = 1e4
options.n_theta_bins = 100
options.phi_symmetry = np.pi / 4
options.I_thresh = 1e-10
options.lookuptable_angles = 200
options.parallel = True
options.c_azimuth = 0.05
options.theta_in = 80 * np.pi / 180
options.phi_in = 0
Si = material('Si')()
GaAs = material('GaAs')()
GaInP = material('GaInP')(In=0.5)
Ag = material('Ag')()
SiN = material('Si3N4')()
Air = material('Air')()
# stack based on doi:10.1038/s41563-018-0115-4
front_materials = [Layer(60e-9, SiN), Layer(50E-9, GaInP), Layer(50e-9, GaAs)]
back_materials = [Layer(100E-9, GaInP), Layer(70e-9, GaAs)]
# whether pyramids are upright or inverted is relative to front incidence.
# so if the same etch is applied to both sides of a slab of silicon, one surface
# will have 'upright' pyramids and the other side will have 'not upright' (inverted)
# pyramids in the model
def i_gaas():
return material(i_gaas_material_name)(T=t)
import numpy as np
import os
import tempfile
import solcore.poisson_drift_diffusion as PDD
from solcore.solar_cell import SolarCell, default_GaAs
from solcore.structure import Layer, Junction
from solcore import si
from solcore import material
from solcore.light_source import LightSource
from solcore.solar_cell_solver import solar_cell_solver
T = 298
substrate = material('GaAs')(T=T)
def AlGaAs(T):
# We create the other materials we need for the device
window = material('AlGaAs')(T=T, Na=5e24, Al=0.8)
p_AlGaAs = material('AlGaAs')(T=T, Na=1e24, Al=0.4)
n_AlGaAs = material('AlGaAs')(T=T, Nd=8e22, Al=0.4)
bsf = material('AlGaAs')(T=T, Nd=2e24, Al=0.6)
output = Junction([
Layer(width=si('30nm'), material=window, role="Window"),
Layer(width=si('150nm'), material=p_AlGaAs, role="Emitter"),
Layer(width=si('1000nm'), material=n_AlGaAs, role="Base"),
Layer(width=si('200nm'), material=bsf, role="BSF")
],
if calculate_absorption:
result_band_edge["alpha"] = calc_alpha(result_band_edge,
**alpha_params)
result_band_edge["alphaE"] = interp1d(x=result_band_edge["alpha"][0],
y=result_band_edge["alpha"][1])
return result_band_edge, bands
if __name__ == "__main__":
from solcore import si, material
from solcore.structure import Layer, Structure
import matplotlib.pyplot as plt
import numpy as np
bulk = material("GaAs")(T=293)
barrier = material("GaAsP")(T=293, P=0.1)
bulk.strained = False
barrier.strained = True
top_layer = Layer(width=si("30nm"), material=bulk)
inter = Layer(width=si("3nm"), material=bulk)
barrier_layer = Layer(width=si("15nm"), material=barrier)
bottom_layer = top_layer
E = np.linspace(1.15, 1.5, 300) * q
alfas = np.zeros((len(E), 6))
alfas[:, 0] = E / q
def qw_mat():
return material(qw_mat_material_name)(T=t, In=qw_mat_in)
def test_43_TMM_absorption_profile(self):
GaAs = material('GaAs')(T=300)
my_structure = Structure([
Layer(si(3000, 'nm'), material=GaAs),
Layer(si(300, 'um'), material=GaAs),
])
out = calculate_absorption_profile(my_structure,
np.array([800]),
z_limit=3000,
steps_size=20)
data = (0.00093920198054733134, 0.00091329190431268755,
0.00088809661793623094, 0.00086359640227330484,
0.00083977208217782804, 0.00081660501149482764,
0.0007940770584669893, 0.00077217059154379222,
0.00075086846558214263, 0.00073015400842769127,
0.00071001100786633451, 0.00069042369893569059,
0.00067137675158661923, 0.00065285525868512457,
0.00063484472434525627, 0.00061733105258387328,
0.00060030053628839001, 0.00058373984648887986,
0.00056763602192612503, 0.00055197645890746013,
0.00053674890144246557, 0.0005219414316507909,
0.00050754246043459913, 0.00049354071840833585,
0.00047992524707871981, 0.00046668539026805409,
0.0004538107857741483, 0.00044129135726031623,
0.00042911730636911071, 0.00041727910505361793,
0.00040576748812031177, 0.00039457344597762961,
0.00038368821758459675, 0.00037310328359397839,
0.00036281035968459497, 0.00035280139007758007,
0.00034306854123150837, 0.00033360419571145685,
0.00032440094622720458, 0.00031545158983589954,
0.00030674912230466134, 0.00029828673262870248,
0.00029005779770068137, 0.00028205587712711315,
0.00027427470818778237, 0.00026670820093421067,
0.00025935043342334711, 0.00025219564708274435,
0.00024523824220360293, 0.00023847277355814448,
0.00023189394613789383, 0.00022549661100952902,
0.0002192757612850577, 0.00021322652820316562,
0.00020734417731867015, 0.00020162410479709653,
0.00019606183381147725, 0.00019065301103855347,
0.0001853934032516379, 0.00018027889400747007,
0.00017530548042447405, 0.00017046927004989423,
0.00016576647781335848, 0.00016119342306448588,
0.00015674652669221659, 0.00015242230832361372,
0.00014821738359994165, 0.00014412846152789071,
0.00014015234190387394, 0.00013628591280938143,
0.00013252614817542982, 0.0001288701054142039,
0.00012531492311603331, 0.00012185781880990432,
0.00011849608678575335, 0.00011522709597683633,
0.00011204828790051968, 0.00010895717465587773,
0.00010595133697653208, 0.00010302842233720751,
0.00010018614311252307, 9.7422274786577231e-05,
9.4734654211925496e-05, 9.2121177916588886e-05,
8.9579800457766819e-05, 8.7108532820967001e-05,
8.470544086329888e-05, 8.2368643799712795e-05,
8.009631273099949e-05, 7.7886669212397987e-05,
7.573798386169243e-05, 7.3648575005706959e-05,
7.1616807364140996e-05, 6.9641090769713272e-05,
6.7719878923614451e-05, 6.5851668185292527e-05,
6.4034996395625842e-05, 6.2268441732560816e-05,
6.0550621598319883e-05, 5.8880191537308663e-05,
5.7255844183874585e-05, 5.5676308239094561e-05,
5.41403474757902e-05, 5.2646759770991723e-05,
5.1194376165094236e-05, 4.9782059946968693e-05,
4.8408705764312587e-05, 4.7073238758543685e-05,
4.5774613723559514e-05, 4.4511814287704784e-05,
4.3283852118305772e-05, 4.2089766148149713e-05,
4.0928621823303459e-05, 3.9799510371682887e-05,
3.8701548091800482e-05, 3.7633875661134488e-05,
3.6595657463578247e-05, 3.5586080935443529e-05,
3.4604355929505788e-05, 3.3649714096593824e-05,
3.2721408284239568e-05, 3.1818711951917646e-05,
3.0940918602416916e-05, 3.0087341228898868e-05,
2.9257311777210295e-05, 2.8450180623029266e-05,
2.7665316063435288e-05, 2.6902103822505617e-05,
2.6159946570550893e-05, 2.5438263456613905e-05,
2.4736489653865175e-05, 2.4054075917540213e-05,
2.3390488155071715e-05, 2.2745207008081107e-05,
2.211772744590139e-05, 2.1507558370314028e-05,
2.0914222231189692e-05, 2.0337254652732693e-05,
1.9776204070036316e-05, 1.9230631375664536e-05,
1.8700109575983667e-05, 1.8184223456974879e-05,
1.7682569259266036e-05, 1.7194754362128619e-05,
1.6720396976192213e-05, 1.6259125844636267e-05,
1.5810579952625165e-05, 1.5374408244759177e-05,
1.4950269350320186e-05, 1.4537831316097222e-05)
for i in range(len(out['absorption'][0])):
self.assertAlmostEqual(data[i], out['absorption'][0][i])
'phi_in': 1e-6,
'I_thresh': 0.001,
#'coherent': True,
#'coherency_list': None,
#'lookuptable_angles': 200,
#'prof_layers': [1,2],
#'n_rays': 100000,
#'random_angles': False,
#'nx': 5, 'ny': 5,
'parallel': True,
'n_jobs': -1,
'phi_symmetry': np.pi / 2,
'only_incidence_angle': True
}
Si = material('Si_OPTOS')()
Air = material('Air')()
# materials with constant n, zero k
x = 1000
d_vectors = ((x, 0), (0, x))
area_fill_factor = 0.36
hw = np.sqrt(area_fill_factor) * 500
front_materials = []
back_materials = [
Layer(si('120nm'),
Si,
geometry=[{
'type': 'rectangle',
import numpy as np
import matplotlib.pyplot as plt
from solcore import material, si
from solcore.solar_cell import SolarCell, Layer, Junction
from solcore.solar_cell_solver import solar_cell_solver, prepare_solar_cell
from solcore.state import State
GaInP = material("GaInP")(In=0.5)
GaAs = material("GaAs")()
Ge = material("Ge")()
optical_struct = SolarCell([
Layer(material=GaInP, width=si("5000nm")),
Junction(
[
Layer(material=GaAs, width=si("200nm")),
Layer(material=GaAs, width=si("5um")),
],
kind="DA",
),
Layer(material=Ge, width=si("50um")),
])
wl = np.linspace(250, 1700, 300) * 1e-9
options = State()
options.wavelength = wl
options.optics_method = "TMM"
options.no_back_reflection = False
search_db('Ag', exact=True)
# This prints out, line by line, matching entries. This shows us entries with
# "pageid"s 0 to 14 correspond to silver.
# Let's compare the optical behaviour of some of those sources:
# pageid = 0, Johnson
# pageid = 2, McPeak
# pageid = 8, Hagemann
# pageid = 12, Rakic (BB)
# create instances of materials with the optical constants from the database.
# The name (when using Solcore's built-in materials, this would just be the
# name of the material or alloy, like 'GaAs') is the pageid, AS A STRING, while
# the flag nk_db must be set to True to tell Solcore to look in the previously
# downloaded database from refractiveindex.info
Ag_Joh = material(name='0', nk_db=True)()
Ag_McP = material(name='2', nk_db=True)()
Ag_Hag = material(name='8', nk_db=True)()
Ag_Rak = material(name='12', nk_db=True)()
Ag_Sol = material(name='Ag')() # Solcore built-in (from SOPRA)
# plot the n and k data. Note that not all the data covers the full wavelength range,
# so the n/k value stays flat.
names = ['Johnson', 'McPeak', 'Hagemann', 'Rakic', 'Solcore built-in']
plt.figure()
plt.plot(wl * 1e9, Ag_Joh.n(wl), wl * 1e9, Ag_McP.n(wl), wl * 1e9,
Ag_Hag.n(wl), wl * 1e9, Ag_Rak.n(wl), wl * 1e9, Ag_Sol.n(wl))
# matrix multiplication
wavelengths = np.linspace(300, 1200, 200)*1e-9
options = default_options
options.nm_spacing = 0.5
options.wavelengths = wavelengths
options.project_name = 'Perovskite_Si_1e6_150wl'
options.n_rays = 100000
options.n_theta_bins = 100
options.phi_symmetry = np.pi/4
options.I_thresh = 1e-4
options.lookuptable_angles = 200
options.parallel = True
options.only_incidence_angle = True
Si = material('Si')()
Air = material('Air')()
MgF2 = material('MgF2_RdeM')()
ITO_back = material('ITO_lowdoping')()
Perovskite = material('Perovskite_CsBr')()
Ag = material('Ag_Jiang')()
aSi_i = material('aSi_i')()
aSi_p = material('aSi_p')()
aSi_n = material('aSi_n')()
LiF = material('LiF')()
IZO = material('IZO')()
C60 = material('C60')()
# materials with constant n, zero k
Spiro = [12e-9, np.array([0,1]), np.array([1.65, 1.65]), np.array([0,0])]
SnO2 = [10e-9, np.array([0,1]), np.array([2, 2]), np.array([0,0])]
pal = sns.cubehelix_palette(15)
cols = cycler('color', pal)
params = {
'legend.fontsize': 'small',
'axes.labelsize': 'small',
'axes.titlesize': 'small',
'xtick.labelsize': 'small',
'ytick.labelsize': 'small',
'axes.prop_cycle': cols
}
plt.rcParams.update(params)
Air = material('Air')()
Si = material('566', nk_db=True)()
size = 5
#for size in [4]:
n_passes = []
nxy = [5, 10, 20, 35, 50]
reps = [1, 2, 4, 10]
calc = True
options = {
'wavelengths': np.arange(300, 1201, 20) * 1e-9,
'theta': 0 * np.pi / 180,
'phi': 0,
'I_thresh': 1e-2,
def test_quantum_mechanics_schrodinger():
""" Testing schrodinger equation solver
"""
bulk = material("GaAs")(T=293)
barrier = material("GaAsP")(T=293, P=0.1)
bulk.strained = False
barrier.strained = True
top_layer = Layer(width=si("30nm"), material=bulk)
inter = Layer(width=si("3nm"), material=bulk)
barrier_layer = Layer(width=si("15nm"), material=barrier)
bottom_layer = top_layer
E = np.linspace(1.15, 1.5, 300) * q
alfas = np.zeros((len(E), 6))
alfas[:, 0] = E / q
alpha_params = {
"well_width": si("7.2nm"),
"theta": 0,
"eps": 12.9 * vacuum_permittivity,
"espace": E,
"hwhm": si("6meV"),
"dimensionality": 0.16,
"line_shape": "Gauss",
}
QW = material("InGaAs")(T=293, In=0.2)
QW.strained = True
well_layer = Layer(width=si("7.2nm"), material=QW)
my_structure = Structure(
[
top_layer,
barrier_layer,
inter,
well_layer,
inter,
barrier_layer,
inter,
bottom_layer,
],
substrate=bulk,
)
band_edge, bands = QM.schrodinger(
my_structure,
quasiconfined=0,
num_eigenvalues=20,
alpha_params=alpha_params,
calculate_absorption=True,
)
for key in band_edge["E"]:
for i in range(len(band_edge["E"][key])):
band_edge["E"][key][i] = solcore.asUnit(band_edge["E"][key][i],
"eV") * 1000
band_edge["E"][key][i] = round(band_edge["E"][key][i])
Ehh = np.all(np.equal(band_edge["E"]["Ehh"], my_energies["Ehh"]))
Elh = np.all(np.equal(band_edge["E"]["Elh"], my_energies["Elh"]))
Ee = np.all(np.equal(band_edge["E"]["Ee"], my_energies["Ee"]))
idx = 100
out = [band_edge["alpha"][0][idx] / q, band_edge["alpha"][1][idx]]
# Test over the energies
assert Ehh and Elh and Ee
# Test over the absorption coefficent at a given energy
for i, data in enumerate(out):
assert out[i] == approx(my_absorption[i], rel=1e-4)
(6.15, 1.69), (5.39, 2.14), (5.89, 1.41), (5.40, 2.49), (5.44, 1.21),
(5.67, 0.73)]
zz = np.linspace(0, 1, 100)
lat = np.zeros_like(zz)
eg = np.zeros_like(zz)
colors = plt.cm.jet(np.linspace(0, 1, len(mat)))
fig = plt.figure(figsize=(6, 4.5))
ax = fig.add_subplot(111)
for j, (m, x) in enumerate(zip(mat, xx)):
for i, z in enumerate(zz):
param = {x: z}
new_mat = material(m)(T=300, **param)
lat[i] = new_mat.lattice_constant * 1e10
eg[i] = asUnit(new_mat.band_gap, 'eV')
ax.plot(lat, eg, label=m, color=colors[j])
lat2 = []
eg2 = []
for (m, p) in zip(mat2, pos):
new_mat = material(m)(T=300)
lat2.append(new_mat.lattice_constant * 1e10)
eg2.append(asUnit(new_mat.band_gap, 'eV'))
ax.annotate(m, xy=p)
import numpy as np
import matplotlib.pyplot as plt
from solcore import material, si
from solcore.solar_cell import SolarCell, Layer, Junction
from solcore.solar_cell_solver import solar_cell_solver, prepare_solar_cell
from solcore.state import State
GaInP = material('GaInP')(In=0.5)
GaAs = material('GaAs')()
Ge = material('Ge')()
optical_struct = SolarCell([Layer(material=GaInP, width=si('5000nm')),
Junction([Layer(material=GaAs, width=si('200nm')),
Layer(material=GaAs, width=si('5um'))], kind = 'DA'),
Layer(material=Ge, width=si('50um'))
])
wl = np.linspace(250, 1700, 300)*1e-9
options = State()
options.wavelength = wl
options.optics_method = 'TMM'
options.no_back_reflexion = False
options.BL_correction = True
options.recalculate_absorption = True
options.theta = 0
from solcore import material
from solcore.structure import Layer, Junction, TunnelJunction
from solcore.solar_cell import SolarCell
from solcore.solar_cell_solver import solar_cell_solver
from solcore.light_source import LightSource
import solcore.poisson_drift_diffusion as PDD
import numpy as np
import matplotlib.pyplot as plt
T = 298
wl = np.linspace(350, 1050, 601) * 1e-9
# First, we create the materials of the QW
QWmat = material('InGaAs')(T=T, In=0.2, strained=True)
Bmat = material('GaAsP')(T=T, P=0.1, strained=True)
i_GaAs = material('GaAs')(T=T)
# The QW is 7 nm wide, with GaAs interlayers 2 nm thick at each side and GaAsP barriers 10 nm thick.
# The final device will have 30 of these QWs.
QW = PDD.CreateDeviceStructure('QW',
T=T,
repeat=30,
substrate=i_GaAs,
layers=[
Layer(width=10e-9,
material=Bmat,
role="barrier"),
Layer(width=2e-9,
material=i_GaAs,
role="interlayer"),
from solcore import si, material
from solcore.structure import Layer, Structure
import matplotlib.pyplot as plt
import numpy as np
import solcore.quantum_mechanics as QM
from solcore.constants import vacuum_permittivity, q
# First we create the materials we need
bulk = material("GaAs")(T=293, strained=False)
barrier = material("GaAsP")(T=293, P=0.1, strained=True)
# As well as some of the layers
top_layer = Layer(width=si("30nm"), material=bulk)
inter = Layer(width=si("3nm"), material=bulk)
barrier_layer = Layer(width=si("15nm"), material=barrier)
bottom_layer = top_layer
E = np.linspace(1.15, 1.5, 300) * q
alfas = np.zeros((len(E), 6))
alfas[:, 0] = E / q
# We define some parameters need to calculate the shape of the excitonic absorption
alpha_params = {
"well_width": si("7.2nm"),
"theta": 0,
"eps": 12.9 * vacuum_permittivity,
"espace": E,
"hwhm": si("6meV"),
"dimensionality": 0.16,
"line_shape": "Gauss"
return 2 * np.pi * (A_back + A_front)
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
from solcore.state import State
from solcore import material
junction = State()
options = State()
wl = np.linspace(300, 900, 300) * 1e-9
GaAs = material('GaAs')(T=300)
n = GaAs.n(wl)
cos_tc = np.sqrt(1 - 1 / n**2)
Rn = ((n - 1) / (n + 1))**2
wlg = 800 * 1e-9
A = 1
V = 0
T = 300
options.wavelength = wl
junction.reflected = interp1d(wl, Rn)
junction.absorptance = lambda x: A * (x < wlg)
#
from solcore import material
from solcore.quantum_mechanics import kp_bands
import numpy as np
# Material parameters
GaAs = material("GaAs")(T=300)
InGaAs = material("InGaAs")
comp = np.linspace(0.01, 0.25, 5)
for i in comp:
InGaAs2 = InGaAs(In=i, T=300)
result = kp_bands(GaAs,
InGaAs2,
graph=True,
fit_effective_mass=True,
effective_mass_direction="L")
import matplotlib.pyplot as plt
import numpy as np
from solcore.absorption_calculator import calculate_absorption_profile
from solcore import material, si
from solcore.structure import Structure, Layer
# First we defined a couple of materials, for example, GaAs and AlGAs
GaAs = material('GaAs')(T=300)
AlGaAs = material('AlGaAs')(T=300, Al=0.3)
# Now, let's build the structure
my_structure = Structure([
Layer(si(30, 'nm'), material=AlGaAs),
Layer(si(3000, 'nm'), material=GaAs),
Layer(si(300, 'um'), material=GaAs),
])
# We want to calculate the absorption profile of this structure as a function of the position and wavelength
x = np.linspace(400, 1000, 200)
out = calculate_absorption_profile(my_structure, x, steps_size=1, z_limit=3000)
# Finally, we plot the absorption profile. Note that absorption at short wavelengths take place near the surface of the
# structure, in the AlGaAs layer and top of the GaAs layer, while longer wavelengths penetrate more. Wavelengths beyond
# the GaAs band edge are not absorbed.
plt.figure(1)
ax = plt.contourf(out['position'], x, out['absorption'], 200)
plt.xlabel('Position (nm)')
plt.ylabel('Wavelength (nm)')
cbar = plt.colorbar()
cbar.set_label('Absorption (1/nm)')
from solcore import material
import numpy as np
import matplotlib.pyplot as plt
import os
# Initiate some variables...
wl = np.linspace(300, 1800, 1000)
Al_fraction = np.linspace(10, 100, 10)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(11.25, 4))
ax1b = ax1.twinx()
# Load GaAs n and k data from the Sopra database...
GaAs = material("GaAs", sopra=True)(T=300)
GaAs_n = GaAs.n(wl * 1e-9)
GaAs_k = GaAs.k(wl * 1e-9)
# Load Ge n and k data from Sopra database...
Ge = material("Ge", sopra=True)(T=300)
Ge_n = Ge.n(wl * 1e-9)
Ge_k = Ge.k(wl * 1e-9)
# Load AlGaAs k data for a range of compositions...
AlGaAs = material("AlGaAs", sopra=True)
AlGaAs_k = [GaAs_k]
for comp in Al_fraction:
AlGaAs_k.append(AlGaAs(T=300, Al=comp / 100).k(wl * 1e-9))
np.imag(stack.get_indices(wavelength)) / wavelength)
data_p = tmm.inc_position_resolved(
layer, d_in_layer, out2, coherency_list, 4 * np.pi *
np.imag(stack.get_indices(wavelength)) / wavelength)
output['absorption'] = 0.5 * (data_s + data_p)
return output
if __name__ == '__main__':
import matplotlib.pyplot as plt
from solcore import material, si
from solcore.structure import Layer, Structure
GaAs = material('GaAs')(T=300)
InGaAs = material('InGaAs')(T=300, In=0.1)
my_structure = Structure([
Layer(si(3000, 'nm'), material=InGaAs),
Layer(si(30, 'um'), material=GaAs),
])
wavelength = np.linspace(450, 1100, 300)
out = calculate_rat(my_structure,
wavelength,
coherent=True,
no_back_reflexion=False)
# #
# plt.plot(wavelength, out['R'], 'b', label='Reflexion')
def test_61_kp_bulk_solvers(self):
GaAs = solcore.material("GaAs")(T=300)
GaAsP = solcore.material("GaAsP")(P=0.3, T=300)
InGaAs = solcore.material("InGaAs")(In=0.2, T=300)
# Testing QM.kp_bands
bands_GaAsP = QM.kp_bands(GaAsP, GaAs, graph=False, fit_effective_mass=True, effective_mass_direction="X",
return_so=True)
bands_InGaAs = QM.kp_bands(InGaAs, GaAs, graph=False, fit_effective_mass=True, effective_mass_direction="X",
return_so=True)
expected_bands_GaAsP = (1.2168382480631407e-19, -1.5452519153253004e-19, -1.4042149045828435e-19,
-1.9192138182935611e-19, 8.0093555784846597e-32, 1.2472835955929216e-31,
2.6423749777877535e-31, 1.2393634061184521e-31)
expected_bands_InGaAs = (8.6764014773233634e-20, -1.0573103504669798e-19, -1.1984351916698905e-19,
-1.6993543036257329e-19, 6.6193386922591731e-32, 1.3576713980579555e-31,
8.0904387208083259e-32, 1.2268187169919973e-31)
for i in range(len(bands_GaAsP)):
self.assertAlmostEqual(bands_GaAsP[i], expected_bands_GaAsP[i])
self.assertAlmostEqual(bands_InGaAs[i], expected_bands_InGaAs[i])
# Testing QM.KPbands and QM.fit_effective_masses
edges_GaAsP = QM.KPbands(GaAsP, GaAs, fraction=0.2, return_edges_only=True)
bands_GaAsP = QM.KPbands(GaAsP, GaAs, fraction=0.2)
masses_GaAsP = QM.fit_effective_masses(bands_GaAsP, GaAsP, GaAs, plot_result=False)
edges_InGaAs = QM.KPbands(InGaAs, GaAs, fraction=0.2, return_edges_only=True)
bands_InGaAs = QM.KPbands(InGaAs, GaAs, fraction=0.2)
masses_InGaAs = QM.fit_effective_masses(bands_InGaAs, InGaAs, GaAs, plot_result=False)
expected_edges_GaAsP = (1.2168382480631407e-19, -1.5452519153253004e-19, -1.4042149045828435e-19,
-1.9192138182935611e-19)
expected_edges_InGaAs = (8.6764014773233634e-20, -1.0573103504669798e-19, -1.1984351916698905e-19,
-1.6993543036257329e-19)
expected_masses_GaAsP = (8.049577422084102e-32, 1.2627430248682043e-31, 2.6577242586172804e-31,
1.2305748108835472e-31)
expected_masses_InGaAs = (6.6895885457875e-32, 1.3994390560400583e-31, 8.142667105522975e-32,
1.2060355194525871e-31)
for i in range(len(edges_GaAsP)):
self.assertAlmostEqual(edges_GaAsP[i], expected_edges_GaAsP[i])
self.assertAlmostEqual(edges_InGaAs[i], expected_edges_InGaAs[i])
self.assertAlmostEqual(masses_GaAsP[i], expected_masses_GaAsP[i])
self.assertAlmostEqual(masses_InGaAs[i], expected_masses_InGaAs[i])
# Testing QM.kp8x8_bulk
bands_GaAsP = QM.kp8x8_bulk(GaAsP, GaAs)
bands_InGaAs = QM.kp8x8_bulk(InGaAs, GaAs)
expected_bands_GaAsP = (1.2168382480631407e-19, -1.5452519153253004e-19, -1.4042149045828435e-19,
-1.9192138182935611e-19, 8.138445281947437e-32, 1.3543674202428507e-31,
2.848952594319033e-31, 1.145125159048442e-31)
expected_bands_InGaAs = (8.6764014773233634e-20, -1.0573103504669798e-19, -1.1984351916698905e-19,
-1.6993543036257329e-19, 6.77957483053393e-32, 1.6988821114765817e-31,
7.820551493038613e-32, 1.1461138067300424e-31)
for i in range(len(bands_GaAsP)):
self.assertAlmostEqual(bands_GaAsP[i], expected_bands_GaAsP[i])
self.assertAlmostEqual(bands_InGaAs[i], expected_bands_InGaAs[i])
# user options
T = 298
wl = si(np.linspace(400, 900, 80), 'nm')
light_source = LightSource(source_type='standard',
version='AM1.5g',
x=wl,
output_units='photon_flux_per_m',
concentration=1)
opts = default_options
opts.wavelength, opts.no_back_reflexion, opts.size, opts.light_source, opts.T_ambient = \
wl, False, [400, 400], light_source, T
# The size of the unit cell for the RCWA structure is 400 x 400 nm
# Defining all the materials we need
Air = material('Air')(T=T)
p_GaAs = material('GaAs')(T=T, Na=si('4e18cm-3')) # for the GaAs cell emitter
n_GaAs = material('GaAs')(T=T, Nd=si('2e17cm-3')) # for the GaAs cell base
AlAs, GaAs = material('AlAs')(T=T), material('GaAs')(T=T) # for the DBR
SiO2 = material('SiO2', sopra=True)(T=T) # for the spacer layer
TiO2 = material('TiO2', sopra=True)(T=T) # for the nanoparticles
# some parameters for the QE solver
for mat in [n_GaAs, p_GaAs]:
mat.hole_mobility, mat.electron_mobility, mat.permittivity = 3.4e-3, 5e-2, 9
n_GaAs.hole_diffusion_length, p_GaAs.electron_diffusion_length = si(
"500nm"), si("5um")
# Define the different parts of the structure we will use. For the GaAs junction, we use the depletion approximation
GaAs_junction = [
Junction([
options = default_options
options.nm_spacing = 0.5
options.wavelengths = wavelengths
options.project_name = 'test_matrix2'
options.n_rays = 50000
options.n_theta_bins = 30
options.phi_symmetry = np.pi/4
options.I_thresh = 1e-5
options.lookuptable_angles = 200
options.parallel = True
options.c_azimuth = 0.001
options.theta_in = 0*np.pi/180
options.phi_in = 'all'
options.only_incidence_angle = False
Ge = material('Ge')()
GaAs = material('GaAs')()
GaInP = material('GaInP')(In=0.5)
Ag = material('Ag')()
SiN = material('Si3N4')()
Air = material('Air')()
Ta2O5 = material('410', nk_db=True)()
MgF2 = material('203', nk_db=True)()
SiGeSn = material('SiGeSn')()
# stack based on doi:10.1038/s41563-018-0115-4
front_materials = [Layer(120e-9, MgF2), Layer(74e-9, Ta2O5), Layer(464e-9, GaInP), Layer(1682e-9, GaAs), Layer(1289e-9, SiGeSn)]
back_materials = [Layer(100E-9, SiN)]
# whether pyramids are upright or inverted is relative to front incidence.
# so if the same etch is applied to both sides of a slab of silicon, one surface
def b_mat():
return material(b_mat_material_name)(T=t, P=b_mat_p)
options['n_theta_bins'] = 100
options['wavelengths'] = wavelengths
options['c_azimuth'] = 0.1
options['phi_symmetry'] = np.pi / 2
options['parallel'] = True
options['pol'] = 'u'
options.project_name = 'testing'
theta_intv, phi_intv, angle_vector = make_angle_vector(options['n_theta_bins'],
options['phi_symmetry'],
options['c_azimuth'])
surf = regular_pyramids(elevation_angle=0, upright=True) # [texture, reverse]
GaAs = material('GaAs')()
Ge = material('Ge')()
Si = material('Si')()
Air = material('Air')()
layers = [Layer(500e-9, GaAs), Layer(200e-9, Ge)]
# optist = OptiStack(layers, False, Si, Air)
# lookup table:
size = ((500, 0), (0, 500))
results_front = RCWA(layers,
size,
orders,
options,
Air,
"""
try:
num = self.junction_indices[junction]
self[num].__dict__.update(kwargs)
except IndexError:
print(
'ERROR updating junction: The junction index must be {} or less.'
.format(len(self.junction_indices)))
def __call__(self, i):
return self[self.junction_indices[i]]
if __name__ == '__main__':
window = material('AlGaAs')(T=298, Na=1e24, Al=0.8)
stack = [Layer(width=si("50nm"), material=window), default_GaAs(298)]
# stack = [default_GaAs(298)]
my_cell = SolarCell(layers=stack)
#
# my_cell.append_multiple([default_GaAs(298), default_GaAs(298), default_GaAs(298)])
# print(my_cell)
from solcore.poisson_drift_diffusion.DriftDiffusionUtilities import solve_pdd, default_photon_flux, \
default_wavelengths
import matplotlib.pyplot as plt
solve_pdd(my_cell, 'QE', vfin=1.2, vstep=0.05, light=True)
QE = my_cell(0).qe
# We need to build the solar cell layer by layer.
# We start from the AR coating. In this case, we load it from an an external file
refl_nm = np.loadtxt(this_dir_file("MgF-ZnS_AR.csv"),
unpack=True,
delimiter=",")
ref = interp1d(x=siUnits(refl_nm[0], "nm"),
y=refl_nm[1],
bounds_error=False,
fill_value=0)
# TOP CELL - GaInP
# Now we build the top cell, which requires the n and p sides of GaInP and a window layer.
# We also load the absorption coefficient from an external file. We also add some extra parameters needed for the
# calculation such as the minority carriers diffusion lengths
AlInP = material("AlInP")
InGaP = material("GaInP")
window_material = AlInP(Al=0.52)
top_cell_n_material = InGaP(In=0.49,
Nd=siUnits(2e18, "cm-3"),
hole_diffusion_length=si("200nm"))
top_cell_p_material = InGaP(In=0.49,
Na=siUnits(1e17, "cm-3"),
electron_diffusion_length=si("1um"))
all_materials.append(window_material)
all_materials.append(top_cell_n_material)
all_materials.append(top_cell_p_material)
# MID CELL - InGaAs
# We add manually the absorption coefficient of InGaAs since the one contained in the database doesn't cover
import tkinter as tk
from tkinter import ttk
from solcore import material
from solcore.material_data import calculate_mobility
from solcore.constants import q
from solcore.solar_cell_solver import rcwa_options, pdd_options, asc_options
# The default material is a GaAs at 298 K and provides a set of minimum properties that are used if they are not
# available for the material of interest and not alternative is given
# Default values MUST BE in SI units
DefaultMaterial = material("GaAs")(T=298)
default_layer_properties = {
'band_gap': DefaultMaterial.band_gap, # J
'electron_affinity': DefaultMaterial.electron_affinity, # J
'eff_mass_electron': DefaultMaterial.eff_mass_electron, # relative to m0
'eff_mass_hh_z': DefaultMaterial.eff_mass_hh_z, # relative to m0
'eff_mass_lh_z': DefaultMaterial.eff_mass_lh_z, # relative to m0
'electron_mobility': calculate_mobility("GaAs", 0, 1), # m2 V-1 s-1
'hole_mobility': calculate_mobility("GaAs", 1, 1), # m2 V-1 s-1
'ni': DefaultMaterial.ni, # m-3
'Nc': DefaultMaterial.Nc, # m-3
'Nv': DefaultMaterial.Nv, # m-3
'electron_minority_lifetime': 3e-6, # s
'hole_minority_lifetime': 2.5e-7, # s
'relative_permittivity':
DefaultMaterial.relative_permittivity, # relative to epsilon0
'electron_auger_recombination': 1e-42, # m6 s-1,
'hole_auger_recombination': 1e-42, # m6 s-1
'radiative_recombination':
def test_rcwa_polygon():
import numpy as np
from solcore import material, si
from solcore.structure import Layer, Structure
from solcore.absorption_calculator.rigorous_coupled_wave import calculate_rat_rcwa, \
calculate_absorption_profile_rcwa
T = 300
Air = material("Air")(T=T)
TiO2 = material("TiO2", sopra=True)(T=T) # for the nanoparticles
GaAs = material("GaAs")(T=T)
th = 50
NP_layer_square = Layer(
si(th, "nm"),
Air,
geometry=[{
"type": "rectangle",
"mat": TiO2,
"center": (200, 200),
"halfwidths": [50, 50],
"angle": 0
}],
)
np_struct_square = Structure([NP_layer_square])
NP_layer_polygon = Layer(
si(th, "nm"),
Air,
geometry=[{
"type": "polygon",
"mat": TiO2,
"center": (200, 200),
"vertices": ((150, 150), (250, 150), (250, 250), (150, 250)),
"angle": 0
}],
)
np_struct_polygon = Structure([NP_layer_polygon])
wl = np.linspace(300, 1000, 10)
step_size = 2
rat_np_sq = calculate_rat_rcwa(np_struct_square,
size=((400, 0), (0, 400)),
orders=10,
wavelength=wl,
substrate=GaAs,
incidence=Air,
pol=[1, 0])
A_output_sq = rat_np_sq['A_pol']
result_square = calculate_absorption_profile_rcwa(np_struct_square,
size=((400, 0), (0,
400)),
orders=10,
wavelength=wl,
rat_output_A=A_output_sq,
parallel=True,
steps_size=step_size,
pol=[1, 0])
rat_np_pol = calculate_rat_rcwa(np_struct_polygon,
size=((400, 0), (0, 400)),
orders=10,
wavelength=wl,
substrate=GaAs,
incidence=Air,
pol=[1, 0])
A_output_pol = rat_np_pol['A_pol']
result_polygon = calculate_absorption_profile_rcwa(
np_struct_polygon,
size=((400, 0), (0, 400)),
orders=10,
wavelength=wl,
rat_output_A=A_output_pol,
parallel=True,
steps_size=step_size,
pol=[1, 0])
assert approx(A_output_sq == A_output_pol)
assert approx(result_polygon["absorption"] == result_square["absorption"])