Precise control of process parameters for >23% efficiency perovskite solar cells in ambient air using an automated device acceleration platform

Abstract

Achieving high-performance perovskite photovoltaics, especially in ambient air, is critically dependent on the precise optimization of process parameters. However, traditional manual methods often struggle to effectively control the key variables. This inherent challenge requires a paradigm shift toward automated platforms capable of precise and reproducible experiments. Herein, we use a fully automated device acceleration platform (DAP) to optimize air-processed parameters for preparing perovskite devices using a two-step sequential deposition technique. Over ten process parameters with significant potential to influence device performance are systematically optimized. Specifically, we delve into the impact of the dripping speed of organic ammonium halide, a parameter that is difficult to control manually, on both perovskite film and device performance. Through the targeted design of experiments, we reveal that the dripping speed significantly affects device performance primarily by adjusting the residual PbI₂ content in the films. We find that optimal dripping speeds, such as 50 µL s⁻¹, contribute to top-performance devices. Conversely, excessively fast or slow speeds result in devices with comparatively poorer performance and lower reproducibility. The optimized parameter set enables us to establish a standard operation procedure (SOP) for additive-free perovskite processing in ambient conditions, which yield devices with efficiencies surpassing 23%, satisfactory reproducibility, and state-of-the-art photo-thermal stability. This research underscores the importance of understanding the causality of process parameters in enhancing perovskite photovoltaic performance. Furthermore, our study highlights the pivotal role of automated platforms in discovering innovative workflows and accelerating the development of high-performing perovskite photovoltaic technologies.