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 PublicationsJournal Article (refereed)peer-review

182 Citations (Scopus)


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 languageEnglish
Pages (from-to)159-172
Number of pages14
JournalIEEE Transactions on Semiconductor Manufacturing
Issue number2
Publication statusPublished - May 2006
Externally publishedYes

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).


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


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