Perovskite solar cells (PSCs) hold great potential as a technology for the next generation of thin-film photovoltaics. For PSCs to be commercially viable, scalable fabrication processes must be improved based on state-of-the-art techniques used for small-area devices. Hence, there is a need to investigate and optimize the formation of perovskite thin films fabricated through scalable deposition methods to facilitate large-scale production of solution-based, large-area PSCs. However, the formation of perovskite thin films from precursor solution is complex and involves the interrelated phases of drying, nucleation, and crystal growth. Therefore, in-depth understanding and precise control of these stages are vital to consistently fabricate high-quality optoelectronic films.

The application of in situ multi-channel photoluminescence (PL) imaging allows recording the temporal evolution of the perovskite thin-film formation, while also providing spatial resolution and spectral information. We have generated and made publicly available unique experimental data that comprises in situ PL data of blade-coated PSCs and corresponding quality measures, such as power conversion efficiency and perovskite layer thickness. Exploring this in situ PL data has the potential to reveal important variations in the thin-film formation process, however, the limits of human analysis are exceeded due to the high dimensionality. Here, machine learning (ML) methods are a promising route to investigate such complex, multi-dimensional PL data to elucidate the large-area formation of blade-coated perovskite thin-films during vacuum quenching.

To accelerate empirical-based traditional, incremental scientific progress, we employ deep learning and explainable artificial intelligence (XAI) techniques to identify correlations between the in situ PL data collected during the perovskite thin-film fabrication and resulting solar cell performance metrics, while making these correlations humanly understandable. Our analysis shows that variations in the quality of PSCs can be understood by examining the thin-film formation process with ML and XAI. By applying various XAI methods we explain not only which data features are important but also provide a data-driven explanation why they are important. Additionally, our research illustrates how these insights can be translated into practical guidelines for optimizing perovskite thin-film fabrication by giving actionable recommendations to experimental scientists. Remarkably, we are able to generate these insights just by analyzing the given dataset without having to perform additional laborious and expensive trial-and-error experiments.

Our research improves the understanding of the complex large-area formation of perovskite thin-films and highlights the pivotal role of ML, and XAI methodologies in particular, in accelerating energy materials research. As XAI methods are not limited to the investigated dataset, this study is a prime example of how similar analyses can be performed to interpret and improve processes in numerous other areas of sustainable materials research.

For an in-depth analysis and further discussion of these findings, see our full research paper “Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI”, L. Klein*, S. Ziegler*, F. Laufer et al., Adv. Mater., 202307160 (2023), doi: 10.1002/adma.202307160.