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Statistical feature extraction and selection for IC test pattern analysis

Content Provider	IEEE Xplore Digital Library
Author	Lin, T.-S. Meador, J.
Copyright Year	1992
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA (Lin, T.-S.; Meador, J.)
Abstract	Complexity reduction and automatic test point selection are discussed in the context of statistical pattern classification. Different types of feedforward neural networks capable of IC fault diagnosis are examined. To reduce diagnostic complexity, principal component analysis (PCA) and full stepwise feature selection are employed to reduce network input dimension without sacrificing accuracy. For fault analysis purposes, it seems that feature selection by stepwise variable selection appears much more useful than feature extraction by PCA, since the latter requires that all original test measurements be made while the former helps eliminate redundant measurements.<>
Starting Page	391
Ending Page	394
File Size	307154
Page Count	4
File Format	PDF
ISBN	0780305930
DOI	10.1109/ISCAS.1992.229931
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-05-10
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Integrated circuit testing Pattern analysis Automatic testing Principal component analysis Circuit faults Neural networks Input variables Circuit testing Feedforward neural networks
Content Type	Text
Resource Type	Article

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