### GISAS by rough interfaces

Scattering from a multilayered sample with correlated roughness.

• The sample is composed of a substrate on which is sitting a stack of layers. These layers consist in a repetition of 5 times two different superimposed layers (from bottom to top):
• layer A: $2.5$ nm thick with a real refractive index $n = 5 \cdot 10^{-6}$.
• layer B: $5$ nm thick with a real refractive index $n = 10 \cdot 10^{-6}$.
• There is no added particle.
• All layers present the same type of roughness on the top surface, which is characterized by:
• a rms roughness of the interfaces $\sigma = 1$ nm,
• a Hurst parameter $H$ equal to $0.3$,
• a lateral correlation length $\xi$ of $5$ nm,
• a cross correlation length $\xi_{\perp}$ equal to $10^{-4}$ nm.
• The incident beam is characterized by a wavelength of 0.1 nm.
• The incident angles are $\alpha_i = 0.2 ^{\circ}$ and $\varphi_i = 0^{\circ}$.

Note:

The roughness profile is described by a normally-distributed random function. The roughness correlation function at the jth interface is expressed as: $$< U_j (x, y) U_j (x’, y’)> = \sigma^2 \frac{2^{1-H}}{\Gamma(H)} \left( \frac{\tau}{ξ} \right)^H K_H \left( \frac{\tau}{ξ} \right), \tau=[(x-x’)^2+(y-y’)^2]^{\frac{1}{2}}$$

• $U_j(x, y)$ is the height deviation of the jth interface at position $(x, y)$.
• $\sigma$ gives the rms roughness of the interface. The Hurst parameter $H$, comprised between $0$ and $1$ is connected to the fractal dimension $D=3-H$ of the interface. The smaller $H$ is, the more serrate the surface profile looks. If $H = 1$, the interface has a non fractal nature.
• The lateral correlation length ξ acts as a cut-off for the lateral length scale on which an interface begins to look smooth. If $\xi \gg \tau$ the surface looks smooth.
• The cross correlation length $\xi_{\perp}$ is the vertical distance over which the correlation between layers is damped by a factor $1/e$. It is assumed to be the same for all interfaces. If $\xi_{\perp} = 0$ there is no correlations between layers. If $\xi_{\perp}$ is much larger than the layer thickness, the layers are perfectly correlated.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59  #!/usr/bin/env python3 """ MultiLayer with correlated roughness """ import bornagain as ba from bornagain import ba_plot as bp, deg, nm def get_sample(): """ A sample with two layers on a substrate, with correlated roughnesses. """ # defining materials vacuum = ba.RefractiveMaterial("ambience", 0, 0) material_part_a = ba.RefractiveMaterial("PartA", 5e-6, 0) material_part_b = ba.RefractiveMaterial("PartB", 10e-6, 0) material_substrate = ba.RefractiveMaterial("substrate", 15e-6, 0) # defining layers l_ambience = ba.Layer(vacuum) l_part_a = ba.Layer(material_part_a, 2.5*nm) l_part_b = ba.Layer(material_part_b, 5*nm) l_substrate = ba.Layer(material_substrate) sigma, hurst, corrLength = 1*nm, 0.3, 5*nm autocorr = ba.K_CorrelationModel(sigma, hurst, corrLength) interlayer = ba.TanhInterlayer() crosscorrelation = ba.CommonDepthCrosscorrelation(10*nm) roughness = ba.LayerRoughness(autocorr, interlayer, crosscorrelation) my_sample = ba.MultiLayer() # adding layers my_sample.addLayer(l_ambience) n_repetitions = 5 for _ in range(n_repetitions): my_sample.addLayerWithTopRoughness(l_part_a, roughness) my_sample.addLayerWithTopRoughness(l_part_b, roughness) my_sample.addLayerWithTopRoughness(l_substrate, roughness) return my_sample def get_simulation(sample): beam = ba.Beam(5e11, 0.1*nm, 0.2*deg) n = 200 detector = ba.SphericalDetector(n, -0.5*deg, 0.5*deg, n, 0., 1*deg) simulation = ba.ScatteringSimulation(beam, sample, detector) return simulation if __name__ == '__main__': sample = get_sample() simulation = get_simulation(sample) result = simulation.simulate() bp.plot_simulation_result(result) bp.show_or_export() 
auto/Examples/scatter2d/CorrelatedRoughness.py