Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
권호 표지
DOI QR코드

DOI QR Code

Classification Performance Analysis of Silicon Wafer Micro-Cracks Based on SVM

SVM 기반 실리콘 웨이퍼 마이크로크랙의 분류성능 분석

  • Kim, Sang Yeon (Graduate School, Aeronautical and Mechanical Design Engineering, Korea National University of Transportation) ;
  • Kim, Gyung Bum (Aeronautical and Mechanical Design Engineering, Korea National University of Transportation)
  • 김상연 (한국교통대학교 대학원 항공.기계설계학과) ;
  • 김경범 (한국교통대학교 항공.기계설계학과)
  • Received : 2016.03.02
  • Accepted : 2016.07.14
  • Published : 2016.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

Keywords

References

  1. Chiou, Y.-C., Liu, J.-Z., and Liang, Y.-T., "Micro Crack Detection of Multi-Crystalline Silicon Solar Wafer Using Machine Vision Techniques," Sensor Review, Vol. 31, No. 2, pp. 154-165, 2011. https://doi.org/10.1108/02602281111110013
  2. Ko, S.-S., Liu, C.-S., and Lin, Y.-C., "Optical Inspection System with Tunable Exposure Unit for Micro-Crack Detection in Solar Wafer," Optik-International Journal for Light and Electron Optics, Vol. 124, No. 19, pp. 4030-4035, 2013. https://doi.org/10.1016/j.ijleo.2012.12.024
  3. Abdelhamid, M., Singh, R., and Omar, M., "Review of Microcrack Detection Techniques for Silicon Solar Cells," IEEE Journal of Photovoltaics, Vol. 4, No. 1, pp. 514-524, 2014. https://doi.org/10.1109/JPHOTOV.2013.2285622
  4. Seo, H. J. and Kim, G. B., "A Study on Classification of Micro-Cracks in Silicon Wafer through the Fusion of Principal Component Analysis and Neural Network," J. Korean Soc. Precis. Eng., Vol. 32, No. 5, pp. 463-470, 2015. https://doi.org/10.7736/KSPE.2015.32.5.463
  5. Bin, Z., Yong, L., and Shao-Wei, X., "Support Vector Machine and Its Application in Handwritten Numeral Recognition," Proc. of 15th International Conference on Pattern Recognition, pp. 720-723, 2000.
  6. Hsu, C.-W. and Lin, C.-J., "A Comparison of Methods for Multi-Class Support Vector Machines," IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425, 2002. https://doi.org/10.1109/72.991427
  7. Takahashi, F. and Abe, S., "Decision-Tree-Based Multiclass Support Vector Machines," Proc. of 9th International Conference on ICONIP, pp. 1418-1422, 2002.
  8. Chang, C.-C. and Lin, C.-J., "LIBSVM: A Library for Support Vector Machines," ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, p. 27, 2011.
  9. Seo, H. J., Kim, G. B., and Kim, S. Y., "Correlation Analysis between Micro-Crack Shape and Near-Infrared Image Pattern of Silicon Wafer," Proc. of KSPE Spring Conference, pp. 903-904, 2014.
  10. Oyallon, E. and Rabin, J., "An Analysis of the SURF Method," Image Processing On Line, Vol. 5, pp. 176-218, 2015. https://doi.org/10.5201/ipol.2015.69
  11. Harris, C., and Stephens, M., "A Combined Corner and Edge Detector," Proc. of Alvey Vision Conference, pp. 147-152, 1988.