Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Local Binary Feature and Adaptive Neuro-Fuzzy based Defect Detection in Solar Wafer Surface

지역적 이진 특징과 적응 뉴로-퍼지 기반의 솔라 웨이퍼 표면 불량 검출

  • 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)
  • 고진석 (한국기술교육대학교 전기전자통신공학부) ;
  • 임재열 (한국기술교육대학교 전기전자통신공학부)
  • Received : 2013.05.27
  • Accepted : 2013.06.17
  • Published : 2013.06.30
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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

References

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