Notes:
1) materials are described in SI units
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda * nm, 'Si') #convert lda to SI unit
msi_fn = interp1d(lda, m,
kind='quadratic') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='quadratic') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.15, 'Air', 'SiO2', 'Bruggeman')
msio2np_ideal_fn = interp1d(
lda, m, kind='quadratic') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.0874, 'Air', 'SiO2', 'Bruggeman')
msio2rough_ideal_fn = interp1d(
lda, m, kind='quadratic') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.15, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2np_fn = interp1d(
lda, m, kind='quadratic') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.0874, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2rough_fn = interp1d(
lda, m, kind='quadratic') # make mat data a FUNCTION of lda, in nm
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda * nm, 'SiC') #convert lda to SI unit
msic_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'Ag') #convert lda to SI unit
mag_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.10, 'Air', 'RC0_1B_SiO2',
'Bruggeman') # 15% ff good
msio2np10_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2np_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'SiC', 'Bruggeman')
msicnp_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 900, 1200, 100, 1000, 200,
inf] # list of layer thicknesses in nm # 500nm Al2O3 good
d_list = [inf, 1500, 0, 0, 0, 200,
inf] # list of layer thicknesses in nm # 500nm Al2O3 good
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda * nm, 'RC0_1B_Si') #convert lda to SI unit
msi_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
#m = datalib.alloy(lda*nm, 0.238, 'Air','SiO2','Bruggeman')
#msio2np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.238, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
#m = datalib.alloy(lda*nm, 0.0674, 'Air','SiO2','Bruggeman')
#msio2rough_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.0674, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2rough_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 837.372, 2621.210, 12.848, 525000,
inf] # list of layer thicknesses in nm
c_list = ['i', 'c', 'c', 'c', 'i', 'i']
theta = 0
T_list = []
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda * nm, 'Si3N4') #convert lda to SI unit
msi3n4_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'Ag') #convert lda to SI unit
mag_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'RC0_1D_Al2O3',
'Bruggeman') # 15% ff good
mal2o3np_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'Si3N4', 'Bruggeman')
msi3n4np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 2000, 4000, 2000, 200,
inf] # list of layer thicknesses in nm # 500nm Al2O3 good
c_list = ['i', 'c', 'c', 'c', 'c', 'i']
theta = 0
#
#
m = datalib.Material_RI(lda * nm, 'Si3N4') #convert lda to SI unit
msi3n4_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'Ag') #convert lda to SI unit
mag_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'SiO2', 'Bruggeman')
msio2np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'Si3N4', 'Bruggeman')
msi3n4np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 800, 2000, 200, inf] # list of layer thicknesses in nm
c_list = ['i', 'c', 'c', 'c', 'i']
theta = 0
T_list = []
R_list = []
A_list = []
for lda0 in lda:
plt.plot(lda/1000, real(m2),'k:', label = 'n VASE stock')
plt.plot(lda/1000, imag(m1),'r', label = 'k model')
plt.plot(lda/1000, imag(m2),'r:', label = 'k VASE stock')
plt.xlabel('Wavelength (um)')
plt.ylabel('')
#plt.title('Refractive index of Al2O3')
plt.legend()
plt.show()
#m = datalib.alloy(lda*nm, 0.36, 'Air','RC0_1D_Al2O3','Bruggeman')
#mal2o3np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
#
#m = datalib.alloy(lda*nm, 0.12, 'Air','RC0_1D_Al2O3','Bruggeman')
#mal2o3rough_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.36, 'Air','RC0_1D_Al2O3','Bruggeman')
mal2o3np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.11, 'Air','RC0_1D_Al2O3','Bruggeman')
mal2o3rough_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 376.30, 2300.39, 4.86, 525000, inf] # list of layer thicknesses in nm
c_list = ['i','c','c','c','i','i']
theta = 0
T_list = [];
R_list = [];
A_list = [];
for lda0 in lda:
# msi = msi_fn(lda0)
# if (msi.imag < 0):
Define materials of interest for layered film simulation
Notes:
1) materials are described in SI units
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda*nm, 'RC0_1B_Si') #convert lda to SI unit
msi_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda*nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.36, 'Air','RC0_1D_Al2O3','MG')
mal2o3np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.12, 'Air','RC0_1D_Al2O3','MG')
mal2o3rough_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 576.30, 2773.39, 4.86, 525000, inf] # list of layer thicknesses in nm
c_list = ['i','i','i','c','i','i']
theta = 0
T_list = [];
R_list = [];
A_list = [];
for lda0 in lda:
n_list = [1, mal2o3rough_ideal_fn(lda0), mal2o3np_ideal_fn(lda0), msio2_fn(lda0), msi_fn(lda0), 1]
inc_tmm_data = tmm.inc_tmm('s',n_list,d_list,c_list,theta,lda0)
A_list.append(tmm.inc_absorp_in_each_layer(inc_tmm_data)) #stores as list of np.arrays
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda * nm, 'Si3N4') #convert lda to SI unit
msi3n4_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'Ag') #convert lda to SI unit
mag_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.15, 'Air', 'a-Al2O3', 'Bruggeman') # 15% ff good
mal2o3np_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'SiO2', 'Bruggeman')
msio2np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.30, 'Air', 'Si3N4', 'Bruggeman')
msi3n4np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 100, 1500, 3000, 200,
inf] # list of layer thicknesses in nm # 500nm Al2O3 good
c_list = ['i', 'c', 'c', 'c', 'c', 'i']
theta = 0
"""
"""
Define materials of interest for layered film simulation
Notes:
1) materials are described in SI units
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda*nm, 'AlOxChar3_1C_Au') #convert lda to SI unit
mau_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.416, 'Air','AlOxChar3_1A','Bruggeman')
mnp_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 5600, 300, inf] # list of layer thicknesses in nm
c_list = ['i','c','c','i']
theta = 0
T_list = [];
R_list = [];
A_list = [];
for lda0 in lda:
# msi = msi_fn(lda0)
# if (msi.imag < 0):
# msi.imag = -msi.imag
# if (msi.real < 0):
# msi.real = -msi.real
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
#m = datalib.alloy(lda*nm, 0.252, 'Air','Si3N4','Bruggeman')
#msi3n4np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'RC0_1B_Si') #convert lda to SI unit
msi_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.245, 'Air', 'RC1_1B_Si3N4', 'Bruggeman')
msi3n4np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
#m = datalib.Material_RI(lda*nm, 'RC1_1B_Si3N4') #convert lda to SI unit
plt.plot(lda, real(m))
plt.plot(lda, imag(m))
#%%
d_list = [inf, 173.603, 0.848, 525000, inf] # list of layer thicknesses in nm
c_list = ['i', 'i', 'c', 'i', 'i']
theta = 0
T_list = []
R_list = []
A_list = []
for lda0 in lda:
"""
nm = 1e-9
lda = linspace(400, 500, 100) # list of wavelengths in nm
m = datalib.Material_RI(lda * nm, 'Si') #convert lda to SI unit
msi_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
ff = np.array([20, 30, 40, 50, 60, 100]) / 100
A = np.zeros((len(lda), len(ff)), dtype=np.float64)
T = np.zeros((len(lda), len(ff)), dtype=np.float64)
R = np.zeros((len(lda), len(ff)), dtype=np.float64)
for idx in range(0, len(ff)):
m = datalib.alloy(lda * nm, ff[idx], 'Air', 'Si', 'Bruggeman')
msi_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 88,
inf] # list of layer thicknesses in nm # 500nm Al2O3 good
c_list = ['i', 'c', 'i']
theta = 0
T_list = []
R_list = []
A_list = []
for lda0 in lda:
n_list = [1, msi_fn(lda0), 1]
inc_tmm_data = tmm.inc_tmm('s', n_list, d_list, c_list, theta, lda0)
A_list.append(tmm.inc_absorp_in_each_layer(
Notes:
1) materials are described in SI units
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
#m = datalib.alloy(lda*nm, 0.252, 'Air','Si3N4','Bruggeman')
#msi3n4np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda*nm, 'RC0_1B_Si') #convert lda to SI unit
msi_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda*nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.252, 'Air','Si3N4','Bruggeman')
msi3n4np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 183.798, 12.848, 525000, inf] # list of layer thicknesses in nm
c_list = ['i','i','c','i','i']
theta = 0
T_list = [];
R_list = [];
A_list = [];
for lda0 in lda:
# msi = msi_fn(lda0)
# if (msi.imag < 0):
# msi.imag = -msi.imag
# if (msi.real < 0):
# msi.real = -msi.real
n_list = [1, msi3n4np_ideal_fn(lda0), msio2_fn(lda0), msi_fn(lda0), 1]
from scipy.interpolate import interp1d, InterpolatedUnivariateSpline
nm = 1e-9
lda = linspace(8500, 9500, 1000) # list of wavelengths in nm
def norm(m):
return (real(m) - min(real(m))) / (max(real(m)) - min(real(m))), (
imag(m) - min(imag(m))) / (max(imag(m)) - min(imag(m)))
#%%
import numpy as np
t_atmosphere = datalib.ATData(lda * 1e-9)
m_mat = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
m_MG1 = datalib.alloy(lda * nm, 0.1, 'Air', 'SiO2', 'MG')
#m_MG2 = datalib.alloy(lda*nm, 0.8, 'Air','SiO2','MG')
m_MG2 = datalib.alloy(lda * nm, 0.1, 'SiO2', 'Air', 'MG')
n_mat, k_mat = norm(m_mat)
idx_max_k_mat = np.argmax(k_mat)
n_MG1, k_MG1 = norm(m_MG1)
idx_max_k_MG1 = np.argmax(k_MG1)
n_MG2, k_MG2 = norm(m_MG2)
idx_max_k_MG2 = np.argmax(k_MG2)
#plt.figure()
#
#plt.plot(lda/1000, k_MG1,'k', label = 'MG1')
m = datalib.Material_RI(lda * nm, 'Si3N4') #convert lda to SI unit
msi3n4_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'Ag') #convert lda to SI unit
mag_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
#m = datalib.alloy(lda*nm, 0.30, 'Air','SiO2','Bruggeman')
#msio2np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.238, 'Air', 'RC0_1B_SiO2', 'Bruggeman')
msio2np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.3, 'Air', 'Si3N4', 'Bruggeman')
msi3n4np_ideal_fn = interp1d(
lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 1000, 2500, 2000, 0, 200,
inf] # list of layer thicknesses in nm
c_list = ['i', 'c', 'c', 'c', 'c', 'c', 'i']
theta = 0
T_list = []
R_list = []
A_list = []
for lda0 in lda:
Define materials of interest for layered film simulation
Notes:
1) materials are described in SI units
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda*nm, 'RC0_1B_Si') #convert lda to SI unit
msi_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda*nm, 'SiO2') #convert lda to SI unit
msio2_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.10, 'Air','SiO2','Bruggeman')
msio2np_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda*nm, 0.0674, 'Air','SiO2','Bruggeman')
msio2rough_ideal_fn = interp1d(lda, m, kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 359.944, 35089.86, 15.62, 525000, inf] # list of layer thicknesses in nm
c_list = ['i','c','c','c','i','i']
theta = 0
T_list = [];
R_list = [];
A_list = [];
for lda0 in lda:
# msi = msi_fn(lda0)
# if (msi.imag < 0):
# msi.imag = -msi.imag
fig1 = plt.figure()
plt.plot(lda, real(m), 'k')
plt.plot(lda, imag(m), 'r')
#sio.savemat('P_SiO2_SiN_Ag.mat', {'P_cool_sio2_sin_np': P_cool_sio2_sin_np})
#%% INTERP MATERIALS!!
ff = np.array([
0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,
95, 100
]) / 100
m_sio2 = np.zeros((len(lda), len(ff)), dtype=np.complex64)
for idx in range(0, len(ff)):
m_sio2[:, idx] = datalib.alloy(lda * nm, ff[idx], 'Air', 'SiO2',
'Bruggeman')
m_sin = np.zeros((len(lda), len(ff)), dtype=np.complex64)
for idx in range(0, len(ff)):
m_sin[:, idx] = datalib.alloy(lda * nm, ff[idx], 'Air', 'Si3N4',
'Bruggeman')
sio.savemat(
'SiO2_Brugg_FF_0_5_100_lda.mat', {
'n_sio2': real(m_sio2),
'k_sio2': imag(m_sio2),
'Wavelengths': lda,
'Fill_Fraction': ff
})
sio.savemat(
'SiN_Brugg_FF_0_5_100_lda.mat', {
Notes:
1) materials are described in SI units
2) materials are stored in datalib
3) materials are output as m = n+j*k
4) materials are iterpolated in datalib based on input lda values
"""
m = datalib.Material_RI(lda * nm, 'AlOxChar_2C_Si') #convert lda to SI unit
msi_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.Material_RI(lda * nm, 'AlOxChar_2C_Oxide') #convert lda to SI unit
moxide_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
m = datalib.alloy(lda * nm, 0.0687, 'Air', 'AlOxChar_2A', 'Bruggeman')
malox_fn = interp1d(lda, m,
kind='linear') # make mat data a FUNCTION of lda, in nm
d_list = [inf, 1463.42, 492.117, 525000,
inf] # list of layer thicknesses in nm
c_list = ['i', 'c', 'c', 'i', 'i']
theta = 0
T_list = []
R_list = []
A_list = []
for lda0 in lda:
# msi = msi_fn(lda0)
# if (msi.imag < 0):
# msi.imag = -msi.imag
# if (msi.real < 0):