Real-space visualization of rough interfaces

The roughness defined for a sample can be turned into real-space height maps, useful e.g. for direct visual comparison with atomic-force-microscopy (AFM) images or as a sanity check on the chosen autocorrelation, cross-correlation, and height-distribution parameters.

RoughnessMap(n_pts_x, n_pts_y, Lx, Ly, sample, i_layer[, seed])

generates one random rough surface, iteratively adjusted to reproduce the given spectrum and height statistics. Algorithm by Pérez-Ràfols & Almqvist, Tribology International 131, 591-604 (2019).

For multilayers, rough interfaces can also be generated together:

maps = ba.RoughnessMap.generateInterfaceMaps(
    n_pts_x, n_pts_y, Lx, Ly, sample, interface_indices[, seed])

The result is a NumPy array with shape (len(interface_indices), n_pts_y, n_pts_x). For example, interface_indices=[1, 2] returns the maps for interfaces 1 and 2 in that order.

Unlike two independent RoughnessMap(...).generate() calls, the stack generator uses the roughness cross-correlation model when one is defined. The lowest rough interface is generated by the regular single-interface IAAFT algorithm. Interfaces above it are then built bottom-up from the nearest generated rough interface below: each Fourier component is split into the inherited part prescribed by the adjacent cross-spectrum and an orthogonal independent residual with the remaining auto-spectrum. This makes replicated long-wavelength features visible while keeping the one-interface generator unchanged.

The requested interface indices only select maps from the result; the whole rough stack is generated internally. Therefore generateInterfaceMaps(..., [i], seed) is not generally equivalent to an independent RoughnessMap(..., i, seed).generate() call for an upper interface. The height distribution is adjusted by IAAFT only for the lowest generated rough interface. Upper interfaces are constrained by the target auto-spectrum and adjacent cross-spectrum; their height statistics are therefore an approximation inherited from the spectral construction.

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#!/usr/bin/env python3
"""
Generates correlated random rough surfaces for two sample interfaces.
"""

import random
import numpy as np
import bornagain as ba
from bornagain import ba_plot as bp, nm, nm2, nm3
from matplotlib import gridspec


FILM_THICKNESS = 25*nm
CORRELATION_SPLIT_FREQ = 0.035/nm


def correlation(a, b):
    return np.corrcoef(a.flatten(), b.flatten())[0, 1]


def frequency_filtered(h, cutoff, keep_low):
    fy = np.fft.fftfreq(h.shape[0], d=Ly/h.shape[0])
    fx = np.fft.fftfreq(h.shape[1], d=Lx/h.shape[1])
    qx, qy = np.meshgrid(fx, fy)
    q = np.hypot(qx, qy)
    mask = q <= cutoff if keep_low else q >= cutoff
    return np.fft.ifft2(np.fft.fft2(h) * mask).real


def psd_line(h):
    factor = X_points*Y_points/np.sqrt(Lx*Ly)
    n = np.floor(X_points/2 + 1).astype(int)
    psd = abs(np.fft.rfft2(h)/factor)**2
    psd_points_x = np.array([psd[0][i] for i in range(1, n)])
    f_points_x = np.array([i/Lx for i in range(1, n)])
    return f_points_x, psd_points_x


def add_map(ax, h, title, vlim):
    im = ax.imshow(h, extent=[0, Lx, 0, Ly], cmap='inferno',
                   vmin=-vlim, vmax=vlim)
    ax.set_title(title)
    ax.set_xlabel('x, nm')
    ax.set_ylabel('y, nm')
    bp.plt.colorbar(im, ax=ax, shrink=0.75)


def add_psd(ax, h):
    f, psd = psd_line(h)
    ax.plot(f, psd)
    ax.set_yscale('log')
    ax.set_xscale('log')
    ax.set_title('PSD')
    ax.set_xlabel('$\\nu, nm^{-1}$')


def add_histogram(ax, h):
    rms = np.std(h)
    ax.hist(h.flatten(), range=[-4*rms, 4*rms], bins=240, color='black')
    ax.set_title('Height histogram')
    ax.set_xlabel('h, nm')
    ax.tick_params(left=False, labelleft=False)


def plot(film_top, substrate_top):
    vlim = max(np.max(np.abs(film_top)), np.max(np.abs(substrate_top)))

    fig = bp.plt.figure(figsize=(11, 6.5))
    gs = gridspec.GridSpec(2, 3)
    gs.update(left=0.07, right=0.97, bottom=0.09, top=0.92,
              wspace=0.35, hspace=0.35)

    add_map(fig.add_subplot(gs[0, 0]), substrate_top,
            'Substrate interface', vlim)
    add_psd(fig.add_subplot(gs[0, 1]), substrate_top)
    add_histogram(fig.add_subplot(gs[0, 2]), substrate_top)

    add_map(fig.add_subplot(gs[1, 0]), film_top,
            'Film top interface', vlim)
    add_psd(fig.add_subplot(gs[1, 1]), film_top)
    add_histogram(fig.add_subplot(gs[1, 2]), film_top)


def get_sample():
    """
    A sample with one layer on a substrate, with partially
    correlated roughnesses.
    """
    # defining materials
    vacuum_color = (0.90, 0.93, 0.97)
    vacuum = ba.RefractiveMaterial("ambience", vacuum_color, 0, 0)
    layer_color = (0.05, 0.62, 0.55)
    layer_mat = ba.RefractiveMaterial("layer", layer_color, 5e-6, 0)
    substrate_color = (0.28, 0.57, 0.82)
    substrate_mat = ba.RefractiveMaterial("substrate", substrate_color, 15e-6, 0)

    # defining roughness
    height_distribution = ba.ErfTransient()
    max_spat_freq = 0.5/nm

    # for layer
    omega = 1*nm3
    a1 = 1
    a2 = 10*nm
    a3 = 100*nm2
    a4 = 1000*nm3
    autocorr_layer = ba.LinearGrowthModel(omega, a1, a2, a3, a4,
                                          max_spat_freq)
    roughness_layer = ba.Roughness(autocorr_layer, height_distribution)

    # for substrate
    sigma = 0.9*nm
    alpha = 0.5
    xi = 35*nm
    autocorr_sub = ba.SelfAffineFractalModel(sigma, alpha, xi,
                                             max_spat_freq)
    roughness_sub = ba.Roughness(autocorr_sub, height_distribution)

    # defining layers
    l_ambience = ba.Layer(vacuum)
    l_layer = ba.Layer(layer_mat, FILM_THICKNESS, roughness_layer)
    l_substrate = ba.Layer(substrate_mat, roughness_sub)

    # adding layers
    my_sample = ba.Sample()
    my_sample.addLayer(l_ambience)
    my_sample.addLayer(l_layer)
    my_sample.addLayer(l_substrate)

    return my_sample


if __name__ == '__main__':

    # sample size
    Lx = 1000*nm
    Ly = 1000*nm
    X_points = 512
    Y_points = 512

    sample = get_sample()
    seed = random.randrange(2**32)  # random every run

    # generate both rough interfaces together
    maps = ba.RoughnessMap.generateInterfaceMaps(
        X_points, Y_points, Lx, Ly, sample, [1, 2], seed)
    film_top = maps[0]
    substrate_top = maps[1]

    film_low = frequency_filtered(film_top, CORRELATION_SPLIT_FREQ, True)
    substrate_low = frequency_filtered(substrate_top, CORRELATION_SPLIT_FREQ,
                                       True)
    film_high = frequency_filtered(film_top, 2*CORRELATION_SPLIT_FREQ, False)
    substrate_high = frequency_filtered(substrate_top,
                                        2*CORRELATION_SPLIT_FREQ, False)

    print("seed =", seed)
    print("correlation model = LinearGrowthModel")
    print("film thickness = {:.1f} nm".format(FILM_THICKNESS))
    print("frequency split = {:.3f} nm^-1".format(CORRELATION_SPLIT_FREQ))
    print("full-height correlation =",
          "{:.3}".format(correlation(film_top, substrate_top)))
    print("low-frequency correlation =",
          "{:.3}".format(correlation(film_low, substrate_low)))
    print("high-frequency correlation =",
          "{:.3}".format(correlation(film_high, substrate_high)))

    plot(film_top, substrate_top)
    ba.showSample3D(sample, sample_size=1000*nm, seed=seed)
    bp.plt.show()
auto/Examples/varia/RoughSurface.py