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|a 10.1107/S2053273322008051
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|a eng
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|a Mareček, David
|e verfasserin
|4 aut
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|a Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 19.10.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © David Mareček et al. 2022.
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|a An approach is presented for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the magnitude of the reciprocal-space vector q, as is commonly done, but as a function of both q and time. The real-space structures extracted from the XRR curves are restricted 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 sevenfold reduction of XRR data points, or if the data are noisy due to a 200-fold reduction in counting times. The 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 less beam damage
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|a Journal Article
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|a X-ray reflectivity
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|a co-refinement
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|a in situ measurement
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|a neural networks
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|a neutron reflectivity
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|a Oberreiter, Julian
|e verfasserin
|4 aut
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|a Nelson, Andrew
|e verfasserin
|4 aut
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|a Kowarik, Stefan
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of applied crystallography
|d 1998
|g 55(2022), Pt 5 vom: 01. Okt., Seite 1305-1313
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|u http://dx.doi.org/10.1107/S2053273322008051
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