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Wafer yield prediction using derived spatial variables
Author(s) -
Dong Hang,
Chen Nan,
Wang Kaibo
Publication year - 2017
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2192
Subject(s) - cluster analysis , covariate , data mining , wafer , lasso (programming language) , computer science , yield (engineering) , spatial analysis , cluster (spacecraft) , field (mathematics) , semiconductor device fabrication , regression analysis , logistic regression , statistics , reliability engineering , artificial intelligence , mathematics , machine learning , engineering , materials science , world wide web , pure mathematics , electrical engineering , metallurgy , programming language
Unreliable chips tend to form spatial clusters on semiconductor wafers. The spatial patterns of these defects are largely reflected in functional testing results. However, the spatial cluster information of unreliable chips has not been fully used to predict the performance in field use in the literature. This paper proposes a novel wafer yield prediction model that incorporates the spatial clustering information in functional testing. Fused LASSO is first adopted to derive variables based on the spatial distribution of defect clusters. Then, a logistic regression model is used to predict the final yield (ratio of chips that remain functional until expected lifetime) with derived spatial covariates and functional testing values. The proposed model is evaluated both on real production wafers and in an extensive simulation study. The results show that by explicitly considering the characteristics of defect clusters, our proposed model provides improved performance compared to existing methods. Moreover, the cross‐validation experiments prove that our approach is capable of using historical data to predict yield on newly produced wafers.