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#!/usr/bin/env python3
"""
Basic example of depth-probe simulation with BornAgain.
Sample layers are Si | Ti | Pt | Ti | TiO2 | D2O.
Beam comes from Si side.
Therefore we model the stack with Si on top.
The z axis points from D2O to Si; z=0 is at the Si/Ti interface.
"""
import bornagain as ba
from bornagain import angstrom, ba_plot as bp, deg, nm
import matplotlib.pyplot as plt
# layer thicknesses in angstroms
t_Ti = 130*angstrom
t_Pt = 320*angstrom
t_Ti_top = 100*angstrom
t_TiO2 = 30*angstrom
# beam data
ai_min = 0 # minimum incident angle
ai_max = 1*deg # maximum incident angle
wl = 10*angstrom # wavelength
# convolution parameters
d_ang = 0.01*ba.deg # spread width for incident angle
# depth position span
z_min = -100*nm
z_max = 100*nm
def get_sample():
"""
Constructs a sample with one resonating Ti/Pt layer
"""
# Materials
material_D2O = ba.RefractiveMaterial("D2O", 0.00010116, 1.809e-12)
material_Pt = ba.RefractiveMaterial("Pt", 0.00010117, 3.01822e-08)
material_Si = ba.RefractiveMaterial("Si", 3.3009e-05, 0)
material_Ti = ba.RefractiveMaterial("Ti", -3.0637e-05, 1.5278e-08)
material_TiO2 = ba.RefractiveMaterial("TiO2", 4.1921e-05, 8.1293e-09)
# Layers
layer_1 = ba.Layer(material_Si)
layer_2 = ba.Layer(material_Ti, 13*nm)
layer_3 = ba.Layer(material_Pt, 32*nm)
layer_4 = ba.Layer(material_Ti, 10*nm)
layer_5 = ba.Layer(material_TiO2, 3*nm)
layer_6 = ba.Layer(material_D2O)
# Sample
sample = ba.Sample()
sample.addLayer(layer_1)
sample.addLayer(layer_2)
sample.addLayer(layer_3)
sample.addLayer(layer_4)
sample.addLayer(layer_5)
sample.addLayer(layer_6)
return sample
def get_simulation(sample):
"""
A depth-probe simulation.
"""
nz = 500
na = 5000
scan = ba.AlphaScan(na, ai_min, ai_max)
scan.setWavelength(wl)
z_axis = ba.EquiDivision("z (nm)", nz, z_min, z_max)
simulation = ba.DepthprobeSimulation(scan, sample, z_axis)
alpha_distr = ba.DistributionGaussian(0, d_ang, 25, 3.)
simulation.addParameterDistribution(
ba.ParameterDistribution.BeamInclinationAngle, alpha_distr)
return simulation
if __name__ == '__main__':
bp.parse_args(aspect='auto')
sample = get_sample()
simulation = get_simulation(sample)
result = simulation.simulate()
bp.plot_simulation_result(result)
plt.show()
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