Deep learning from imbalanced data for automatic defect detection in multicrystalline solar wafer images

Abstract

This paper presents a deep learning method for automated defect inspection in multicrystalline solar wafer surfaces. A multicrystalline solar wafer contains local crystal grains with random shapes, sizes and gray-values. It shows a heterogeneous texture in the surface, and makes the automated optical inspection task very challenging. The conventional machine vision needs individually handcraft specific texture features for different types of defects in the silicon solar wafer. The deep learning technique is an end-to-end training. It can learn discriminant features and classification simultaneously. However, it needs a large amount of defect-free and defective samples for the model training. The defect samples are generally not sufficient in the manufacturing process. A generative adversarial network based model is used to generate synthetic defect samples from a small set of real defects. The synthesized defective and true defect-free samples are then used to train a convolutional neural network (CNN) model for defect detection. It overcomes the imbalanced class data problem arising in the CNN model training. The proposed method is compared with the commonly-used learning strategies for imbalanced data. Experimental results show that the proposed deep learning approach can be applied to a variety of defect types found in the multicrystalline solar wafer surface.