Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication

Felix Laufer; Markus Götz; Ulrich W. Paetzold

doi:10.1039/d4ee03445g

journal article Open Access Jan 01, 2025

Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication

Felix Laufer

Markus Götz

Ulrich W. Paetzold

Energy Environ. Sci. Vol. 18 No. 4 pp. 1767-1782 · Royal Society of Chemistry (RSC)

View at Publisher Save 10.1039/d4ee03445g

Abstract

Augmenting characterization methods with deep learning and other machine learning methods allows the identification of material inconsistencies, device performance predictions, and the generation of in situ AI recommendations.

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Details

Published: Jan 01, 2025
Vol/Issue: 18(4)
Pages: 1767-1782
License: View

Authors

F

Felix Laufer

Light Technology Institute, Karlsruhe Institute of Technology, Engesserstrasse 13, 76131 Karlsruhe, Germany

M

Markus Götz

Scientific Computing Center (SCC), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Helmholtz AI, Germany

U

Ulrich W. Paetzold

Light Technology Institute, Karlsruhe Institute of Technology, Engesserstrasse 13, 76131 Karlsruhe, Germany; Institute of Microstructure Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

Funding

European Research Council Award: LAMI-PERO, Project 101087673

Helmholtz Association Award: ZT-I- PF-5-106 (AI-INSU-PERO)

Bundesministerium für Wirtschaft und Klimaschutz Award: 03EE1123A (SHAPE)

Cite This Article

Felix Laufer, Markus Götz, Ulrich W. Paetzold (2025). Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication. Energy Environ. Sci., 18(4), 1767-1782. https://doi.org/10.1039/d4ee03445g

Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication

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