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- W2022100940 abstract "It is known that statistical analysis of test data can help screen potential test escapes without additional physical measurements. Based on analysis of production test data, this paper focuses on feature engineering for statistical tests to screen test escapes. The features are engineered in two aspects: development of effective features and transformation of features into a different space in which the inherent difference between the test escapes and the normal population can be compacted into a small number of features. In feature development, we generate two sets of features to characterize a chip based on the amounts of the chip's test measurements deviated from the measurement means and their amounts deviated from the spatial patterns among dies on the same wafer. In feature transformation, the features are projected into the canonical space, in which the separation between the test escapes and the good chips are encapsulated into the first few dimensions. We show that each set of features reveals a unique set of test escapes, and the transformation of features can result in significant runtime reduction while keeping a comparable differentiating power as that in the original features. Therefore, both sets of features should be utilized and the canonical transformation should be applied when developing statistical tests for test escape reduction." @default.
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- W2022100940 date "2014-10-01" @default.
- W2022100940 modified "2023-09-28" @default.
- W2022100940 title "Feature engineering with canonical analysis for effective statistical tests screening test escapes" @default.
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- W2022100940 doi "https://doi.org/10.1109/test.2014.7035344" @default.
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