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Predicting IC Defect Level Using Diagnosis

Authors: Cheng Xue; R. D. (Shawn) Blanton;

Predicting IC Defect Level Using Diagnosis

Abstract

Predicting defect level (DL) using fault coverage is an extremely difficult task but if can be accomplished ensures high quality while controlling test cost. Because IC testing now involves generating and combining tests from multiple fault models, it is important to understand how the coverage from each fault model relates to the overall DL. In this work, a new model is proposed which learns the effectiveness of fault models from the diagnostic results of defective chips, and predicts defect level using the derived measures of effectiveness and fault cover ages of multiple fault models. The model is verified using fail data from an IBM ASIC and virtual fail data created through simulation. Experiment results demonstrate that this new model can predict DL more reliably than conventional approaches.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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