Multiblock Principal Component Analysis Based on a Combined Index for Semiconductor Fault Detection and Diagnosis

Gregory A. CHERRY; S. Joe QIN

doi:10.1109/TSM.2006.873524

Multiblock Principal Component Analysis Based on a Combined Index for Semiconductor Fault Detection and Diagnosis

Gregory A. CHERRY^*, S. Joe QIN

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

186 Citations (Scopus)

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 T² 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. © 2006 IEEE.

Original language	English
Pages (from-to)	159-172
Number of pages	14
Journal	IEEE Transactions on Semiconductor Manufacturing
Volume	19
Issue number	2
DOIs	https://doi.org/10.1109/TSM.2006.873524
Publication status	Published - May 2006
Externally published	Yes

Bibliographical note

This work was supported in part by the National Science Foundation under Grant CTS-9985074 and by the sponsors of the Texas–Wisconsin Modeling and Control Consortium (TWMCC).

Keywords

Combined index
Contribution plots
Fault detection
Fault diagnosis
Multiblock principal component analysis
Recursive principal component analysis

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10.1109/TSM.2006.873524

Cite this

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title = "Multiblock Principal Component Analysis Based on a Combined Index for Semiconductor Fault Detection and Diagnosis",

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 T2 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. {\textcopyright} 2006 IEEE.",

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author = "CHERRY, {Gregory A.} and QIN, {S. Joe}",

note = "This work was supported in part by the National Science Foundation under Grant CTS-9985074 and by the sponsors of the Texas–Wisconsin Modeling and Control Consortium (TWMCC).",

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KW - Contribution plots

KW - Fault detection

KW - Fault diagnosis

KW - Multiblock principal component analysis

KW - Recursive principal component analysis

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Multiblock Principal Component Analysis Based on a Combined Index for Semiconductor Fault Detection and Diagnosis

Abstract

Bibliographical note

Keywords

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