NDLI: Weighted principal component analysis and its applications to improve FDC performance

Content Provider	IEEE Xplore Digital Library
Author	Yue, H.H. Tomoyasu, M.
Copyright Year	2004
Description	Author affiliation: Tokyo Electron AT Ltd, Yamanashi, Japan (Yue, H.H.; Tomoyasu, M.)
Abstract	This paper discusses several practical issues encountered when applying principal component analysis (PCA) to fault detection and classification (FDC). Those issues presented a challenge to the success of PCA-model based process monitoring. Weighted PCA is proposed to address the issues. Two forms of weighted PCA are discussed: sample-wise and variable-wise. Sample-wise weighted PCA is used to address issues with model updating. By adapting models with process changes, the long-term validity of a PCA model can be maintained. We also propose to deal with drift and shift features separately. Variable-wise weighted PCA is used to incorporate process and sensor knowledge. PCA models built this way require less maintenance and result in better FDC performance. We performed comparison studies on plasma etcher FDC. We have shown that weighted PCA can adapt to process drift, reduce the occurrence of false alarms and make the models easy to maintain.
Starting Page	4262
Ending Page	4267
File Size	204937
Page Count	6
File Format	PDF
ISBN	0780386825
ISSN	01912216
DOI	10.1109/CDC.2004.1429421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-14
Publisher Place	Bahamas
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Etching Plasma applications Plasma materials processing Fault detection Monitoring Semiconductor device manufacture Integrated circuit yield Integrated circuit manufacture Electrical fault detection
Content Type	Text
Resource Type	Article

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