SSVM(Stepwise-Support Vector Machine)을 이용한 반도체 수율 예측A Yields Prediction in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine
- Other Titles
- A Yields Prediction in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine
- Authors
- 안대웅; 고효헌; 백준걸; 김지현; 김성식
- Issue Date
- 2009
- Publisher
- 대한산업공학회
- Keywords
- support vector machine; semiconductor yield classification; semiconductor manufacturing
process
- Citation
- 산업공학(IE interfaces), v.22, no.3, pp.252 - 262
- Indexed
- KCI
- Journal Title
- 산업공학(IE interfaces)
- Volume
- 22
- Number
- 3
- Start Page
- 252
- End Page
- 262
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/121091
- ISSN
- 1225-0996
- Abstract
- 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.
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