Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI
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Datum
2024-02-15Typ
- Journal Article
ETH Bibliographie
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Abstract
Large-area processing of perovskite semiconductor thin-films is complex and evokes unexplained variance in quality, posing a major hurdle for the commercialization of perovskite photovoltaics. Advances in scalable fabrication processes are currently limited to gradual and arbitrary trial-and-error procedures. While the in situ acquisition of photoluminescence (PL) videos has the potential to reveal important variations in the thin-film formation process, the high dimensionality of the data quickly surpasses the limits of human analysis. In response, this study leverages deep learning (DL) and explainable artificial intelligence (XAI) to discover relationships between sensor information acquired during the perovskite thin-film formation process and the resulting solar cell performance indicators, while rendering these relationships humanly understandable. The study further shows how gained insights can be distilled into actionable recommendations for perovskite thin-film processing, advancing toward industrial-scale solar cell manufacturing. This study demonstrates that XAI methods will play a critical role in accelerating energy materials science. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000648553Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Advanced MaterialsBand
Seiten / Artikelnummer
Verlag
Wiley-VCHThema
deep learning; energy materials science; explainable artificial intelligence (XAI); knowledge discovery; perovskite solar cellsOrganisationseinheit
03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ETH Bibliographie
yes
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