Premium
Fast fitting of reflectivity data of growing thin films using neural networks
Author(s) -
Greco Alessandro,
Starostin Vladimir,
Karapanagiotis Christos,
Hinderhofer Alexander,
Gerlach Alexander,
Pithan Linus,
Liehr Sascha,
Schreiber Frank,
Kowarik Stefan
Publication year - 2019
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576719013311
Subject(s) - x ray reflectivity , millisecond , thin film , artificial neural network , computation , materials science , surface finish , scattering , computer science , formalism (music) , reflectivity , optics , computational physics , biological system , algorithm , physics , nanotechnology , artificial intelligence , composite material , art , musical , visual arts , astronomy , biology
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.