Monitoring General Linear Profiles Using Multivariate EWMA schemes

摘要：

We propose a statistical process control (SPC) scheme that can be implemented in industrial practice, where the quality of a process can be characterized by a general linear profile. We start by reviewing the general linear profile model and the existing monitoring methods. Based on that, a novel multivariate exponentially weighted moving average monitoring (MEWMA) scheme is proposed for such a profile. Three enhancement features are introduced to further improve the performance of the proposed scheme, which include 1) the variable sampling interval, 2) the self-starting function, and 3) the parametric diagnostic approach. Throughout this paper, a deep reactive ion etching (DRIE) example from semiconductor manufacturing, which has a profile that fits a quadratic polynomial regression model well, is used to illustrate the implementation of the proposed approach.

作者: Changliang Zou Fugee Tsung Zhaojun Wang

作者单位: Department of Statistics School of Mathematical Sciences Nankai University Tianjian, PR China Department of Industrial Engineering and Logistics Management Hong Kong University of Science and Te Department of Statistics School of Mathematical Sciences Nankai University Tianjian, PR China LPMC o

会议类型: 国际会议

会议名称: 2006 International Conference on Design of Experiments and Its Applications(2006实验设计及其应用国际会议)

会议地点: 天津

会议语种:英文

在线出版日期: 2006-07-09（万方平台首次上网日期，不代表论文的发表时间）