NDLI: Application of PCA for efficient multivariate FDC of semiconductor manufacturing equipment

Content Provider	IEEE Xplore Digital Library
Author	Thieullen, A. Ouladsine, M. Pinaton, J.
Copyright Year	2013
Description	Author affiliation: STMicroelectron. Rousset Process Control Syst., Rousset, France (Pinaton, J.) \|\| LSIS, Aix-Marseille Univ., Marseille, France (Thieullen, A.; Ouladsine, M.)
Abstract	With the evolutions in sensing technologies and the increasing use of advanced process control techniques, terabytes of data are recorded today from manufacturing equipment during the process of semiconductor devices. These large amounts of data are then operated by FDC systems to assess the overall condition of the equipment. In this paper, we consider the Exponential Hybrid-wise Multiway Principal Components Analysis (E-HMPCA), a PCA-derived model that include an Exponentially Weighted Moving Average component, for the condition monitoring of a Chemical Vapor Deposition tool in STMicroelectronics Rousset 8” fab. In order to work directly on temporal signal from equipment sensors, the application of Dynamic Time Warping for data synchronization is also presented. A real-occurred failure case is used to highlight the benefits of this approach on detection efficiency improvement and monitoring complexity reduction.
Sponsorship	IEEE Electron Devices. Soc.
Starting Page	332
Ending Page	337
File Size	876203
Page Count	6
File Format	PDF
ISBN	9781467350068
ISSN	10788743
e-ISBN	9781467350075
DOI	10.1109/ASMC.2013.6552755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Sensors Synchronization Principal component analysis Monitoring Process control Indexes Dynamic Time Warping Fault Detection and Classification Principal Component Analysis Exponentially Weighted Moving Average
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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