@article{gledocs_11858_8746,
author = {Greco, Alessandro and Starostin, Vladimir and Karapanagiotis, Christos and Hinderhofer, Alexander and Gerlach, Alexander and Pithan, Linus and Liehr, Sascha and Schreiber, Frank and Kowarik, Stefan},
title = {Fast fitting of reflectivity data of growing thin films using neural networks},
year = {2019-11-08},
volume = {52},
number = {6},
pages = {1342-1347},
publisher = {International Union of Crystallography},
publisher = {5 Abbey Square, Chester, Cheshire CH1 2HU, England},

abstract = {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.},
note = { \url {http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8746}},
}