. 2023 Nov 1;30(Pt 6):1064-1075.
doi: 10.1107/S160057752300749X.
Epub 2023 Oct 17.
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J Synchrotron Radiat.
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Abstract
Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.
Keywords:
XRR; autonomous experiments; beamline control; closed-loop control; machine learning; reflectometry.
open access.
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Deep learning at the edge enables real-time streaming ptychographic imaging.
Nat Commun. 2023 Nov 3;14(1):7059. doi: 10.1038/s41467-023-41496-z.
Nat Commun. 2023.PMID: 37923741
Free PMC article.
References
-
-
Allan, D., Caswell, T., Campbell, S. & Rakitin, M. (2019). Synchrotron Radiat. News, 32(3), 19–22.
-
-
-
Andrejevic, N., Chen, Z., Nguyen, T., Fan, L., Heiberger, H., Zhou, L.-J., Zhao, Y.-F., Chang, C.-Z., Grutter, A. & Li, M. (2022). Appl. Phys. Rev. 9, 011421.
-
-
-
Babu, A. V., Zhou, T., Kandel, S., Bicer, T., Liu, Z., Judge, W., Ching, D. J., Jiang, Y., Veseli, S., Henke, S., Chard, R., Yao, Y., Sirazitdinova, E., Gupta, G., Holt, M. V., Foster, I. T., Miceli, A. & Cherukara, M. J. (2022). arXiv:2209.09408.
-
-
-
Barty, A., Gutt, C., Lohstroh, W., Murphy, B., Schneidewind, A., Grunwaldt, J.-D., Schreiber, F., Busch, S., Unruh, T., Bussmann, M., Fangohr, H., Görzig, H., Houben, A., Kluge, T., Manke, I., Lützenkirchen-Hecht, D., Schneider, T. R., Weber, F., Bruno, G., Einsle, O., Felder, C., Herzig, E. M., Konrad, U., Markötter, H., Rossnagel, K., Sheppard, T. & Turchinovich, D. (2023). DAPHNE4NFDI – Consortium Proposal, https://doi.org/10.5281/ZENODO.8040606.
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