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Study on Vacuum Pump Monitoring Using MPCA Statistical Method  

Sung D. (Seoul National University)
Kim J. (Seoul National University)
Jung W. (Seoul National University)
Lee S. (Seoul National University)
Cheung W. (Korea Research Institute of Standards and science)
Lim J. (Korea Research Institute of Standards and science)
Chung K. (Korea Research Institute of Standards and science)
Publication Information
Journal of the Korean Vacuum Society / v.15, no.4, 2006 , pp. 338-346 More about this Journal
Abstract
In semiconductor process, it is so hard to predict an exact failure point of the vacuum pump due to its harsh operation conditions and nonlinear properties, which may causes many problems, such as production of inferior goods or waste of unnecessary materials. Therefore it is very urgent and serious problem to develop diagnostic models which can monitor the operation conditions appropriately and recognize the failure point exactly, indicating when to replace the vacuum pump. In this study, many influencing factors are totally considered and eventually the monitoring model using multivariate statistical methods is suggested. The pivotal algorithms are Multiway Principal Component Analysis(MPCA), Dynamic Time Warping Algorithm(DTW Algorithm), etc.
Keywords
Vacuum pump; Batch; Multiway principal component analysis; Dynamic time warping algorithm; Hotelling's $T^2$;
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