산업공학 (IE interfaces)

  • 제22권3호
  • /
  • Pages.252-262
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  • 2009
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  • 1225-0996(pISSN)
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  • 2234-6465(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
권호 표지

SSVM(Stepwise-Support Vector Machine)을 이용한 반도체 수율 예측

A Yields Prediction in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine

  • 안대웅 ((주)하이닉스반도체) ;
  • 고효헌 (고려대학교정보경영공학부) ;
  • 김지현 (광운대학교경영대학) ;
  • 백준걸 (고려대학교정보경영공학부) ;
  • 김성식 (고려대학교정보경영공학부)
  • An, Dae-Wong (Hynix Semiconductor) ;
  • Ko, Hyo-Heon (Division of Information Management Engineering, Korea University) ;
  • Kim, Ji-Hyun (School of Business Administration, Kwangwoon University) ;
  • Baek, Jun-Geol (Division of Information Management Engineering, Korea University) ;
  • Kim, Sung-Shick (Division of Information Management Engineering, Korea University)
  • 투고 : 2009.06.26
  • 심사 : 2009.08.11
  • 발행 : 2009.09.01
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초록

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.

키워드

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