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단계적 SVM(Support Vector Machine)을 이용한 반도체 제조공정에서의 최종검사 공정 수율 예측 방법론
안대웅(Daewoong An),고효헌(Hyo-Heon Ko),백준걸(Jungeol Baek),김성식(Sung-Shick Kim) 한국경영과학회 2009 한국경영과학회 학술대회논문집 Vol.2009 No.5
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, for detecting high and low yields. Stepwise-SVM 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 stepwise-SVM in the yield classification. The experimental results show that stepwise-SVM provides a promising alternative to yield classification for the field data.
단계적 SVM(Support Vector Machine)을 이용한 반도체 제조공정에서의 최종검사 공정 수율 예측 방법론
안대웅(Daewoong An),고효헌(Hyo-Heon Ko),백준걸(Jungeol Baek),김성식(Sung-Shick Kim) 대한산업공학회 2009 대한산업공학회 춘계학술대회논문집 Vol.2009 No.5
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, for detecting high and low yields. Stepwise-SVM 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 stepwise-SVM in the yield classification. The experimental results show that stepwise-SVM provides a promising alternative to yield classification for the field data.