Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Under Sampling for Imbalanced Data using Minor Class based SVM (MCSVM) in Semiconductor Process

MCSVM을 이용한 반도체 공정데이터의 과소 추출 기법

  • Pak, Sae-Rom (School of Industrial Management Engineering, Korea University) ;
  • Kim, Jun Seok (School of Industrial Management Engineering, Korea University) ;
  • Park, Cheong-Sool (School of Industrial Management Engineering, Korea University) ;
  • Park, Seung Hwan (School of Industrial Management Engineering, Korea University) ;
  • Baek, Jun-Geol (School of Industrial Management Engineering, Korea University)
  • 박새롬 (고려대학교 산업경영공학과) ;
  • 김준석 (고려대학교 산업경영공학과) ;
  • 박정술 (고려대학교 산업경영공학과) ;
  • 박승환 (고려대학교 산업경영공학과) ;
  • 백준걸 (고려대학교 산업경영공학과)
  • Received : 2014.04.28
  • Accepted : 2014.05.29
  • Published : 2014.08.15
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Abstract

Yield prediction is important to manage semiconductor quality. Many researches with machine learning algorithms such as SVM (support vector machine) are conducted to predict yield precisely. However, yield prediction using SVM is hard because extremely imbalanced and big data are generated by final test procedure in semiconductor manufacturing process. Using SVM algorithm with imbalanced data sometimes cause unnecessary support vectors from major class because of unselected support vectors from minor class. So, decision boundary at target class can be overwhelmed by effect of observations in major class. For this reason, we propose a under-sampling method with minor class based SVM (MCSVM) which overcomes the limitations of ordinary SVM algorithm. MCSVM constructs the model that fixes some of data from minor class as support vectors, and they can be good samples representing the nature of target class. Several experimental studies with using the data sets from UCI and real manufacturing process represent that our proposed method performs better than existing sampling methods.

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References

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