Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement
© David Mareček et al. 2022.
| Veröffentlicht in: | Journal of applied crystallography. - 1998. - 55(2022), Pt 5 vom: 01. Okt., Seite 1305-1313 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Journal of applied crystallography |
| Schlagworte: | Journal Article X-ray reflectivity co-refinement in situ measurement neural networks neutron reflectivity |
| Zusammenfassung: | © David Mareček et al. 2022. 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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| Beschreibung: | Date Revised 19.10.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0021-8898 |
| DOI: | 10.1107/S2053273322008051 |