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A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process

主成分分析 故障检测与隔离 计算机科学 灵敏度(控制系统) 断层(地质) 数据预处理 数据挖掘 工程类 电子工程 人工智能 地震学 执行机构 地质学
作者
Barry M. Wise,Neal B. Gallagher,Stephanie Watts Butler,Daniel D. White,Gabriel G. Barna
出处
期刊:Journal of Chemometrics [Wiley]
卷期号:13 (3-4): 379-396 被引量:276
标识
DOI:10.1002/(sici)1099-128x(199905/08)13:3/4<379::aid-cem556>3.0.co;2-n
摘要

Journal of ChemometricsVolume 13, Issue 3-4 p. 379-396 Research Article A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process Barry M. Wise, Corresponding Author Barry M. Wise bmw@eigenvector.com Eigenvector Research, Inc., 830 Wapato Lake Road, Manson, WA 98831, USAEigenvector Research, Inc., 830 Wapato Lake Road, Manson, WA 98831, USASearch for more papers by this authorNeal B. Gallagher, Neal B. Gallagher Eigenvector Research, Inc., 830 Wapato Lake Road, Manson, WA 98831, USASearch for more papers by this authorStephanie Watts Butler, Stephanie Watts Butler Texas Instruments, 13536 North Central Expressway, MS 944, Dallas, TX 75265, USASearch for more papers by this authorDaniel D. White Jr, Daniel D. White Jr Texas Instruments, 13536 North Central Expressway, MS 944, Dallas, TX 75265, USASearch for more papers by this authorGabriel G. Barna, Gabriel G. Barna Texas Instruments, 13536 North Central Expressway, MS 944, Dallas, TX 75265, USASearch for more papers by this author Barry M. Wise, Corresponding Author Barry M. Wise bmw@eigenvector.com Eigenvector Research, Inc., 830 Wapato Lake Road, Manson, WA 98831, USAEigenvector Research, Inc., 830 Wapato Lake Road, Manson, WA 98831, USASearch for more papers by this authorNeal B. Gallagher, Neal B. Gallagher Eigenvector Research, Inc., 830 Wapato Lake Road, Manson, WA 98831, USASearch for more papers by this authorStephanie Watts Butler, Stephanie Watts Butler Texas Instruments, 13536 North Central Expressway, MS 944, Dallas, TX 75265, USASearch for more papers by this authorDaniel D. White Jr, Daniel D. White Jr Texas Instruments, 13536 North Central Expressway, MS 944, Dallas, TX 75265, USASearch for more papers by this authorGabriel G. Barna, Gabriel G. Barna Texas Instruments, 13536 North Central Expressway, MS 944, Dallas, TX 75265, USASearch for more papers by this author First published: 14 July 1999 https://doi.org/10.1002/(SICI)1099-128X(199905/08)13:3/4<379::AID-CEM556>3.0.CO;2-NCitations: 212AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Multivariate statistical process control (MSPC) tools have been developed for monitoring a Lam 9600 TCP metal etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibit a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables (e.g. pressure, gas flow rates and power), spatially resolved optical emission spectroscopy (OES) of the plasma and a radio-frequency monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems is presented and the strengths and weaknesses of the methods are discussed, along with the relative advantages of each of the sensor systems. Copyright © 1999 John Wiley & Sons, Ltd. Citing Literature Volume13, Issue3-4Special Issue: Proceedings of the International Chemometrics Research Meeting, May 1998, Veldhoven, The NetherlandsMay ‐ August 1999Pages 379-396 RelatedInformation
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