AI

Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation

Abstract

In photovoltaic (PV) materials development, the complex relationship between device performance and underlying materials parameters obfuscates experimental feedback from current-voltage (J-V) characteristics alone. Here, we address this complexity by adding temperature and injection dependence and applying a Bayesian inference approach to extract multiple device-relevant materials parameters simultaneously. Our approach is an order of magnitude faster than the cumulative time of multiple individual spectroscopy techniques, with added advantages of using device-relevant materials stacks and interface conditions. We posit that this approach could be broadly applied to other semiconductor- and energy-device problems of similar complexity, accelerating the pace of experimental research.