Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
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Published:2023-10-17
Issue:6
Volume:30
Page:1064-1075
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ISSN:1600-5775
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Container-title:Journal of Synchrotron Radiation
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language:
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Short-container-title:J Synchrotron Radiat
Author:
Pithan LinusORCID, Starostin VladimirORCID, Mareček David, Petersdorf Lukas, Völter Constantin, Munteanu Valentin, Jankowski MaciejORCID, Konovalov OlegORCID, Gerlach AlexanderORCID, Hinderhofer AlexanderORCID, Murphy BridgetORCID, Kowarik StefanORCID, Schreiber FrankORCID
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.
Funder
Bundesministerium für Bildung und Forschung Deutsche Forschungsgemeinschaft
Publisher
International Union of Crystallography (IUCr)
Subject
Instrumentation,Nuclear and High Energy Physics,Radiation
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