Fast fitting of reflectivity data of growing thin films using neural networks.
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ABSTRACT:
SUBMITTER: Greco A
PROVIDER: S-EPMC6878882 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature
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Fast fitting of reflectivity data of growing thin films using neural networks.
Greco Alessandro A Starostin Vladimir V Karapanagiotis Christos C Hinderhofer Alexander A Gerlach Alexander A Pithan Linus L Liehr Sascha S Schreiber Frank F Kowarik Stefan S
Journal of applied crystallography 20191108 Pt 6
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 <i>a priori</i> knowle ...[more]
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