Fast fitting of reflectivity data of growing thin films using neural networks

• Published in: Journal of applied crystallography 2019-12, Vol.52 (6), p.1342-1347
• Main Authors: Greco, Alessandro, Starostin, Vladimir, Karapanagiotis, Christos, Hinderhofer, Alexander, Gerlach, Alexander, Pithan, Linus, Liehr, Sascha, Schreiber, Frank, Kowarik, Stefan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Copper compounds; Electronics industry; Least mean squares; Least mean squares algorithm; machine learning; Metal phthalocyanines; Neural networks; Organic semiconductors; organic semi‐conductors; Reflectance; Research Papers; Silica; Silicon dioxide; Thin films; X‐ray reflectivity
• ISSN: 1600-5767; 0021-8898; 1600-5767
• DOI: 10.1107/S1600576719013311

X‐ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and α‐sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed. Artificial neural networks trained with simulated data are shown to correctly and quickly determine film parameters from experimental X‐ray reflectivity curves.