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#!/usr/bin/env python3
"""
Simulation of grating using very long boxes and 1D lattice.
Monte-carlo integration is used to get rid of
large-particle form factor oscillations.
"""
import bornagain as ba
from bornagain import angstrom, ba_plot as bp, deg, micrometer, nm
from matplotlib import pyplot as plt
def get_sample(lattice_rotation_angle=0*deg):
"""
A sample with a grating on a substrate.
lattice_rotation_angle = 0 - beam parallel to grating lines
lattice_rotation_angle = 90*deg - beam perpendicular to grating lines
"""
# Material
material_si = ba.RefractiveMaterial("Si", 5.7816e-6, 1.0229e-7)
box_length, box_width, box_height = 50*micrometer, 70*nm, 50*nm
lattice_length = 150*nm
# collection of particles
interference = ba.Interference1DLattice(
lattice_length, 90*deg - lattice_rotation_angle)
pdf = ba.Profile1DGauss(450)
interference.setDecayFunction(pdf)
box_ff = ba.LongBoxLorentz(box_length, box_width, box_height)
box = ba.Particle(material_si, box_ff)
box.rotate(ba.RotationZ(lattice_rotation_angle))
particle_layout = ba.ParticleLayout()
particle_layout.addParticle(box)
particle_layout.setInterference(interference)
sigma, hurst, corrLength = 5*nm, 0.5, 10*nm
autocorr = ba.K_CorrelationModel(sigma, hurst, corrLength)
interlayer = ba.TanhInterlayer()
roughness = ba.LayerRoughness(autocorr, interlayer)
# assembling the sample
vacuum_layer = ba.Layer(ba.Vacuum())
vacuum_layer.addLayout(particle_layout)
substrate_layer = ba.Layer(material_si, roughness)
sample = ba.Sample()
sample.addLayer(vacuum_layer)
sample.addLayer(substrate_layer)
return sample
def get_simulation(sample):
beam = ba.Beam(1e8, 1.34*angstrom, 0.4*deg)
n = 401
detector = ba.SphericalDetector(n, -0.5*deg, 0.5*deg, n, 0, 0.5*deg)
simulation = ba.ScatteringSimulation(beam, sample, detector)
simulation.options().setMonteCarloIntegration(True, 100)
return simulation
if __name__ == '__main__':
sample = get_sample()
simulation = get_simulation(sample)
if not "__no_terminal__" in globals():
simulation.setTerminalProgressMonitor()
result = simulation.simulate()
peaks = ba.FindPeaks(result, 2, "nomarkov", 0.001)
xpeaks = [peak[0] for peak in peaks]
ypeaks = [peak[1] for peak in peaks]
print(peaks)
plt.plot(xpeaks,
ypeaks,
marker='x',
linestyle='none',
color='white',
markersize=10)
bp.plot_simulation_result(result)
plt.show()
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