Multilevel statistical process control of asynchronous multi-stream processes in semiconductor manufacturing.
ABSTRACT In semiconductor manufacturing, the purpose of chamber matching is the alignment of process and yield results of distinct chambers performing in parallel the same process step on different silicon wafers. In this paper, multi-level linear models and statistical process control techniques are jointly employed to define control charts for monitoring chamber matching accuracy and preemptively report chamber misalignments. Specifically, multilevel versions of the classic T2 Control Chart, MEWMA Control Chart and Self-Starting Control Chart are defined and tested against experimental and simulated data.
- SourceAvailable from: cyut.edu.tw01/2007; Wiley-India.
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ABSTRACT: In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal. Copyright © 2006 John Wiley & Sons, Ltd.Quality and Reliability Engineering 07/2007; 23(5):517 - 543. · 0.68 Impact Factor
Article: Monitoring Multiple Stream Processes[show abstract] [hide abstract]
ABSTRACT: In industry, processes with multiple streams or gauges in parallel are common. We discuss monitoring such processes to detect changes in both the overall process mean and changes in the individual stream or gauge means. We propose two new control chart statistics based on an F test and a likelihood ratio test. One appealing aspect of these approaches is that they can be implemented either with or without process parameter estimates obtained from previous data (i.e., from phase 1 implementation of the control chart). These proposals are shown to compare favorably to available methods. The article is motivated by a truck assembly process in which wheel alignment characteristics are measured on every truck by one of four alignment machines, arranged in parallel within the overall process.Quality Engineering. 07/2008; 20(3):296-308.