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#!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = ["bornagain>=25,<26"]
# ///
"""
Reflectivity of a multilayer, taking into account beam angular divergence
and beam footprint correction, simulated with BornAgain and GenX.
"""
from pathlib import Path
import numpy as np, sys
import bornagain as ba
ba.require_versions("bornagain>=25,<26")
from bornagain import angstrom, deg, nm
# input parameters
wavelength = 1.54*angstrom
beam_sample_ratio = 0.01 # beam-to-sample size ratio
def reference_data(filename):
"""
Loads and returns reference data from GenX simulation
"""
# Data files are next to this script.
data_dir = Path(__file__).resolve().parent
filepath = data_dir / filename
print(f"Loading GenX reference data from {filepath}")
ax_values, data = np.loadtxt(filepath,
usecols=(0, 1),
skiprows=3,
unpack=True)
# translate axis values from double incident angle to incident angle
ax_values *= 0.5
return ax_values, data
def get_sample():
"""
Twenty alternating Ti and Ni layers on a silicon substrate.
"""
ambient_mat = ba.Vacuum()
ti_mat = ba.SLDMaterial("Ti", (0.05, 0.62, 0.55), -1.9493e-6, 0)
ni_mat = ba.SLDMaterial("Ni", (0.93, 0.48, 0.14), 9.4245e-6, 0)
substrate_mat = ba.SLDMaterial(
"SiSubstrate", (0.28, 0.57, 0.82), 2.0704e-6, 0)
stack = ba.LayerStack(10)
stack.addLayer(ba.Layer(ti_mat, 3*nm))
stack.addLayer(ba.Layer(ni_mat, 7*nm))
sample = ba.Sample()
sample.addLayer(ba.Layer(ambient_mat))
sample.addStack(stack)
sample.addLayer(ba.Layer(substrate_mat))
return sample
def get_simulation(sample):
"""
A specular simulation with beam and detector defined.
"""
n = 500
footprint = ba.FootprintSquare(beam_sample_ratio)
alpha_distr = ba.DistributionGaussian(0, 0.01 * deg, 25, 3.)
# scan starts high enough that all divergence samples stay above the horizon
scan = ba.AlphaScan(n, 0.04*deg, 2*deg)
scan.setWavelength(1.54*angstrom)
scan.setFootprint(footprint)
scan.setGrazingAngleDistribution(alpha_distr)
return ba.SpecularSimulation(scan, sample)
if __name__ == '__main__':
genx_axis, genx_values = reference_data("genx_angular_divergence.dat.gz")
ba.plt.yscale('log')
ba.plt.plot(genx_axis, genx_values, 'ko', markevery=300)
sample = get_sample()
ba.showSample3D(sample, sample_size=120*nm, seed=0)
simulation = get_simulation(sample)
result = simulation.simulate()
ba.plot_datafield(result)
ba.plt.legend(['GenX', 'BornAgain'], loc='upper right')
ba.plt.show()
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