Authors:
Du-Ming Tsai
1
;
Morris S. K. Fan
2
;
Yi-Quan Huang
1
and
Wei-Yao Chiu
1
Affiliations:
1
Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Road, Chung-Li, Taiwan and Republic of China
;
2
Department of Industrial Engineering and Management, National Taipei University of Technology, 1 Sec. 3 Zhongxiao E. Rd., Taipei, Taiwan and Republic of China
Keyword(s):
Defect Detection, Multicrystalline Solar Wafer, Saw Mark, Deep Learning.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
Abstract:
This paper presents a machine vision-based scheme to automatically detect saw-mark defects in solar wafer surfaces. A saw-mark defect is a severe flaw when cutting a silicon ingot into wafers. A multicrystalline solar wafer surface presents random shapes, sizes and orientations of crystal grains in the surface and, thus, results in a heterogeneous texture. It makes the automatic visual inspection task extremely difficult. The deep learning technique is an ideal choice to tackle the problem, but it requires a huge amount of positive (defect-free) and negative (defective) samples for the training. The negative samples are generally not sufficient enough in a manufacturing process. We thus apply a GAN-based model to generate the defective samples for training, and then use the true defect-free samples and the synthesized defective samples to train a CNN model. It solves the imbalanced data arising in manufacturing inspection. The preliminary experiment has shown promising results of the
proposed method for detecting various saw-mark defects including black line, white line, and impurity in multicrystalline solar wafers.
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