Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

Abstract

Solar cells based on mono-like cast silicon (MLC-Si) have been attracting increasing attention in the photovoltaic (PV) market due to their high energy conversion efficiency and low cost. As in the production of monocrystalline silicon (MC-Si) and polycrystalline silicon (PC-Si) cells, various defects will inevitably occur during the production process of MLC-Si cells. Although computer vision technology has been employed for defect detection in the production processes, it is still difficult to achieve high accuracy in detecting defects in PV cells using traditional machine vision methods due to defect similarity and complex background. To address this challenge, a deep-learning-based quality assessment algorithm of MLC-Si wafers is proposed. Focusing on the dislocation defects, four different deep learning models are used to conduct migration learning and selected different optimizers (ADAM and SGDM) are used to optimize the network models, achieving good results in evaluating and comparing the quality of ML-Si wafers. On this basis, an improved network model MVGG-19 based on the VGG-19 is designed to improve the prediction accuracy further. The experimental results show that the prediction error of the improved network model is reduced by 63% (compared with VGG-19) and the reasoning speed reaches 10.22 FPS, indicating good detection performance.