Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI

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. The study utilizes deep learning and explainable artificial intelligence (XAI) to understand and optimize the perovskite thin-film formation process for scalable solar cell manufacturing. Based on the findings of the algorithms and the interpretations of the material scientists, can be derived new insights and recommendations paving the way toward improved industrial-scale solar cell manufacturing.image

Automated summary

This study utilizes deep learning and explainable artificial intelligence to understand and optimize the perovskite thin-film formation process, providing new insights and recommendations for scalable solar cell manufacturing.