Spatially Resolved Parameter Mapping and Degradation of Perovskite Solar Cells

Perovskite solar cells have made significant strides towards maturity; however, two major challenges persist: stability and up-scalability. Achieving uniform deposition methods is crucial for ensuring the suitability of devices for large-scale production. Furthermore, a comprehensive understanding of degradation mechanisms is essential for mitigating these effects and improving stability. In this study, we introduce a novel approach that integrates photoluminescence imaging with drift diffusion simulations which incorporate the effects of mobile ions. By employing machine learning techniques, we generate inferred maps of various material parameters. These maps are then tracked throughout the aging process to analyse their spatially resolved temporal evolution during degradation, enabling us to distinguish between bulk. Our findings reveal that macroscopic-scale spatial defects propagate outward, ultimately resulting in device degradation across the entire area. By distinguishing between bulk and surface degradation, our technique provides an additional dimension of insight into the heterogeneous nature of degradation in perovskite solar cells. This enables meaningful comparisons and enhanced comprehension of different treatments and processes. We expect analysis using our approach to enable the field to target sources of inhomogeneity and degradation more precisely, paving the way towards commercialisation.