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Local Binary Feature and Adaptive Neuro-Fuzzy based Defect Detection in Solar Wafer Surface  

Ko, JinSeok (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education)
Rheem, JaeYeol (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Semiconductor & Display Technology / v.12, no.2, 2013 , pp. 57-61 More about this Journal
Abstract
This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.
Keywords
Defect detection; Solar wafer; Local binary feature; Automated visual inspection;
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