Accelerating defect analysis of solar cells via machine learning of the modulated transient photovoltage
Yusheng Li, Yiming Li, Jiangjian Shi, Licheng Lou, Xiao Lan Xu, Yuqi Cui, Jionghua Wu, Dongmei Li, Yanhong Luo, Huijue Wu, Qing Shen, Qingbo Meng
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.