期刊名称：Case Studies in Business, Industry and Government Statistics
印刷版ISSN：2152-372X
出版年度：2007
卷号：1
期号：1
页码：61-70
出版社：Bentley University
摘要：Multivariate control charts can be used effectively to monitor the quality of complex processes with several critical variables simultaneously. However, when the covariance matrix has large dimension in comparison to the number of runs available for parameter estimation, these charts can perform poorly. We incorporate prior information about the covariance matrix in which the number of parameters is reduced to just two. We con-sider a passivation process for semiconductor manufacturing, where each of the variables represents a value at a specific location in a passivation tube, and because of the interaction between the plasma and the reactant gases flowing down the tube, the correlation among the variables might decay with distance between these loca-tions. Moreover, the variability at the locations might be taken equal, further reducing the number of parame-ters. We use a Bayesian method to construct the multivariate control chart, and a statistic, analogous to Hotel-ling's 2T, is used for charting.
关键词：Average Run Length; Correlation; Hotelling’s2T; Multivariate Control Chart; Semi-conductor Data.
