PhD Seminar "Experimental and Solid State Physics"

D. Mareček
Institute of Chemistry, University of Graz, Heinrichstraße 28/IV, 8010 Graz, Austria
david.marecek@uni-graz.at

We present an approach for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the reciprocal space vector q as is commonly done, but as a function of both q and time. We restrict the real-space structures extracted from the XRR curves to be solutions of a physics-informed growth model, and use state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting to co-refine multiple time-dependent XRR curves R(q,t) of a thin film growth experiment. Thereby it becomes possible to correctly analyze XRR data with a fidelity corresponding to standard fits of individual XRR curves even if they are sparsely sampled with a 7-fold reduction of XRR datapoints, or if the data is noisy due to a 200-fold reduction in counting times. Our approach of using a CNN analysis and of including prior information through a kinetic model is not limited to growth studies, but can be easily extended to other kinetic X-ray or neutron reflectivity data to enable faster measurements with lower beam damage.

Zeit: Montag, 30.5.2022
Ort: HS 05.01
Lehrveranstaltung: 653.123 PHD-Seminar zu Experimental and Solid State Physics

Der Vortrag kann in Person im Hörsaal oder online via uniMEET besucht werden:

https://unimeet.uni-graz.at/b/ban-fcm-ygv-mn7

Abstract

Programmvorschau PhD Seminar SS 2022