Polycrystalline silicon wafer defect segmentation based on deep convolutional neural networks

Defect segmentation is an important way for defect detection in machine vision. For polycrystalline silicon wafer production, it is difficult to automatically segment defects due to its inhomogeneous background and unpredictable defect shapes. The conventional methods based on handcrafted models or features not only heavily rely on the expertise, but also are not flexible from one application case to another. In this paper, we propose a defect segmentation method for polycrystalline silicon wafer based on the deep convolutional networks. Firstly, we apply Region Proposal Network (RPN) to generate underlying defect regions. Then, these generated image patches are processed to suitable sizes for feeding into a improved segmentation network which is modified based on U-net and a dilation convolution. Real defect images are used to test the proposed method and experimental results show that proposed method achieves better performance compared with the state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.