Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data. Issue 7 (16th February 2023)

• Record Type: Journal Article
• Authors: Laufer, Felix; Ziegler, Sebastian; Schackmar, Fabian; Moreno Viteri, Edwin A.; Götz, Markus; Debus, Charlotte; Isensee, Fabian; Paetzold, Ulrich W.
• Is Part Of: Solar RRL. Volume 7:Issue 7(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-16
• Subjects: clustering -- datasets -- in situ characterization -- machine learning -- performance prediction -- perovskite solar cells -- process monitoring; Solar energy -- Periodicals; Photovoltaic power generation -- Periodicals; Solar energy -- Research -- Periodicals; Photovoltaic power generation -- Research -- Periodicals; Periodicals; 333.7923
• DOI: 10.1002/solr.202201114
• Languages: English
• ISSNs: 2367-198X

Abstract

Abstract : Large‐area processing remains a key challenge for perovskite solar cells (PSCs). Advanced understanding and improved reproducibility of scalable fabrication processes are required to unlock the technology's economic potential. In this regard, machine learning (ML) methods have emerged as a promising tool to accelerate research and unlock the control needed to produce large‐area solution‐processed perovskite thin films. However, a suitable dataset allowing the analysis of a scalable fabrication process is currently missing. Herein, a unique labeled in situ photoluminescence (PL) dataset for blade‐coated PSCs is introduced and explored with unsupervised k‐means clustering, demonstrating the feasibility to derive meaningful insights from such data. Correlations between the obtained clusters and the measured performance of PSC reveal that the in situ PL signal encodes information about the perovskite thin‐film quality. Detrimental mechanisms during thin‐film formation are detected by identifying spatial differences in PL patterns and, consequently, of device performance. In addition, k‐nearest neighbors is applied to predict the performance of PSCs, motivating further investigations into ML‐based in‐line process monitoring of scalable PSC fabrication to detect, understand, and ultimately minimize process variations across iterations. Abstract : A unique dataset containing in situ photoluminescence data captured during the perovskite thin‐film formation is analyzed using machine learning (ML). To improve understanding of underlying experimental processes, unsupervised ML is applied for data exploration revealing correlations between the in situ data and solar cell performance. Performing initial predictions of solar cell metrics shows the potential of ML‐based in‐line process monitoring.