Accelerating defect analysis of solar cells via machine learning of the modulated transient photovoltage

Abstract

Fast and non-destructive analysis of material defect is a crucial demand for semiconductor devices. Herein, we are devoted to exploring a solar-cell defect analysis method based on machine learning of the modulated transient photovoltage (m-TPV) measurement. The perturbation photovoltage generation and decay mechanism of the solar cell is firstly clarified for this study. High-throughput electrical transient simulations are further carried out to establish a database containing millions of m-TPV curves. This database is subsequently used to train an artificial neural network to correlate the m-TPV and defect properties of the perovskite solar cell. A Back Propagation neural network has been screened out and applied to provide a multiple parameter defect analysis of the cell. This analysis reveals that in a practical solar cell, compared to the defect density the charge capturing cross-section plays a more critical role in influencing the charge recombination properties. We believe this defect analysis approach will play a more important and diverse role for solar cell studies.