NDLI: Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Cherry, G.A. Qin, S.J.
Copyright Year	1988
Abstract	The purposes of multivariate statistical process control (MSPC) are to improve process operations by quickly detecting when process abnormalities have occurred and diagnosing the sources of the process abnormalities. In the area of semiconductor manufacturing, increased yield and improved product quality result from reducing the amount of wafers produced under suboptimal operating conditions. This paper presents a complete MSPC application method that combines recent contributions to the field, including multiway principal component analysis (PCA), recursive PCA, fault detection using a combined index, and fault contributions from Hotelling's T/sup 2/ statistic. In addition, a method for determining multiblock fault contributions to the combined index is introduced. The effectiveness of the system is demonstrated using postlithography metrology data and plasma stripper processing tool data.
Sponsorship	IEEE Electron Devices Society IEEE Components, Packaging, and Manufacturing Technology Society IEEE Reliability Society IEEE Solid-State Circuits Society
Starting Page	159
Ending Page	172
Page Count	14
File Size	590247
File Format	PDF
ISSN	08946507
Volume Number	19
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Fault detection Fault diagnosis Metrology Monitoring Process control Plasma materials processing Signal design Plasma measurements Semiconductor device manufacture recursive principal component analysis Combined index contribution plots fault detection fault diagnosis multiblock principal component analysis
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Condensed Matter Physics Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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