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

Laufer, Felix 1; Götz, Markus ORCID iD icon 2; Paetzold, Ulrich W. ORCID iD icon 1,3
1 Lichttechnisches Institut (LTI), Karlsruher Institut für Technologie (KIT)
2 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)
3 Institut für Mikrostrukturtechnik (IMT), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Reproducible large-area fabrication is one of the remaining challenges for the commercialization of perovskite photovoltaics. Imaging methods augmented with deep learning (DL) enable in-line detection of spatial or temporal inconsistencies and predict the impact of observed changes on device performance. In this work, we showcase three use cases of how DL augments complex experimental data analysis of the large-area perovskite thin film formation, even on moderate-sized datasets. First, we demonstrate material composition monitoring by differentiating between precursor property variations, ensuring material consistency during fabrication. Second, we provide early thin-film quality assessment by predicting holistic device performance even before its finalization. Finally, we extend the approach from parameter prediction to generating recommendations for process control by forecasting monitoring signals as a function of a variable process parameter and predicting the corresponding device performances. By addressing tasks that are hardly possible for humans to solve, we present how DL augments data analysis by transforming experimental data into predictions of target parameters.

Zugehörige Institution(en) am KIT Institut für Mikrostrukturtechnik (IMT)
Lichttechnisches Institut (LTI)
Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2025
Sprache Englisch
Identifikator ISSN: 1754-5692, 1754-5706
KITopen-ID: 1000178479
HGF-Programm 38.01.05 (POF IV, LK 01) Simulations, Theory, Optics and Analytics (STOA)
Weitere HGF-Programme 46.21.04 (POF IV, LK 01) HAICU
Erschienen in Energy & Environmental Science
Verlag Royal Society of Chemistry (RSC)
Nachgewiesen in Scopus
Web of Science
Dimensions

Verlagsausgabe §
DOI: 10.5445/IR/1000178479
Veröffentlicht am 28.01.2025
Seitenaufrufe: 21
seit 29.01.2025
Downloads: 10
seit 29.01.2025
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