Statistical feature extraction and selection for IC test pattern analysis

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