TY  - JOUR
A1  - Greco, Alessandro
A1  - Starostin, Vladimir
A1  - Karapanagiotis, Christos
A1  - Hinderhofer, Alexander
A1  - Gerlach, Alexander
A1  - Pithan, Linus
A1  - Liehr, Sascha
A1  - Schreiber, Frank
A1  - Kowarik, Stefan
T1  - Fast fitting of reflectivity data of growing thin films using neural networks
Y1  - 2019-11-08
VL  - 52
IS  - 6
SP  - 1342
EP  - 1347
JF  - Journal of Applied Crystallography
DO  - 10.1107/S1600576719013311
DO  - 10.23689/fidgeo-4400
PB  - International Union of Crystallography
N2  - 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.
N2  - Artificial neural networks trained with simulated data are shown to correctly and quickly determine film parameters from experimental X‐ray reflectivity curves.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8746
ER  -