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AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz

AKPIK 1: AKPIK Talks

AKPIK 1.2: Vortrag

Montag, 16. März 2020, 17:00–17:15, HSZ 301

Fast fitting of reflectivity data of growing thin films using neural networks — •Alessandro Greco¹, Vladimir Starostin¹, Christos Karapanagiotis², Alexander Hinderhofer¹, Alexander Gerlach¹, Linus Pithan³, Sascha Liehr⁴, Frank Schreiber¹, and Stefan Kowarik⁴ — ¹Institut für Angewandte Physik, University of Tübingen, Auf der Morgenstelle 10, Tübingen 72076, Germany — ²Institut für Physik, Humboldt Universität zu Berlin, Newtonstrasse 15, Berlin 12489, Germany — ³European Synchrotron Radiation Facility, 71 Avenue des Martyrs, Grenoble 38000, France — ⁴Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin 12205, Germany

X-ray reflectometry is a powerful and popular scattering technique that can give valuable insight into the structure and growth behavior of thin films. This study [1] shows how a simple artificial neural network model can be used to determine the thickness, roughness and electron density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and α-sexithiophene] on SiO₂ from their reflectivity 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.

[1] Greco et al., J. Appl. Cryst. (2019). 52, 1342–1347