%0 Journal article
%A Greco, Alessandro
%A Starostin, Vladimir
%A Karapanagiotis, Christos
%A Hinderhofer, Alexander
%A Gerlach, Alexander
%A Pithan, Linus
%A Liehr, Sascha
%A Schreiber, Frank
%A Kowarik, Stefan
%T Fast fitting of reflectivity data of growing thin films using neural networks
%R 10.1107/S1600576719013311
%R 10.23689/fidgeo-4400
%J Journal of Applied Crystallography
%V 52
%N 6

%I International Union of Crystallography
%X 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.
%U http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8746
%~  FID GEO-LEO e-docs