An Accurate Description of Pre-Bias Space-Charge-Limited Current Measurements in Perovskite Thin Films for Accurate Charge Transport Properties: A New Machine Learning-Based Experimental-Computational Study

SCLC measurements are widely used to characterize charge transport in semiconductor thin films, including perovskites. However, the effects of intrinsic ion migration in perovskite on SCLC measurements are unknown, leading to inaccurate charge transport characterization. As an alternative, pre-bias SCLC methods were introduced, but no detailed description of how to interpret them exists to date. This project aims to develop detailed descriptions of pre-bias SCLC theory in perovskite. The study will be experimental-computational. We will create a new machine learning-based device simulation to study ion migration. The project's findings will give scientists a device simulation tool and a new SCLC data fitting method for determining perovskite charge transport. This new method will speed up the development of perovskite materials for solar cells.