Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Machine Learning
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Speedpath prediction based on learning from a small set of examples
Proceedings of the 45th annual Design Automation Conference
Statistical diagnosis of unmodeled systematic timing effects
Proceedings of the 45th annual Design Automation Conference
Speedpath analysis based on hypothesis pruning and ranking
Proceedings of the 46th Annual Design Automation Conference
Association rule mining: models and algorithms
Association rule mining: models and algorithms
Data mining in design and test processes: basic principles and promises
Proceedings of the 2013 ACM international symposium on International symposium on physical design
Custom on-chip sensors for post-silicon failing path isolation in the presence of process variations
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Due to the magnitude and complexity of design and manufacturing processes, it is unrealistic to expect that models and simulations can predict all aspects of silicon behavior accurately. When unexpected behavior is observed in the post-silicon stage, one desires to identify the causes and consequently identify the fixes. This paper studies one formulation of the design-silicon mismatch problem. To analyze unexpected behavior, silicon behavior is partitioned into two classes, one class containing instances of unexpected behavior and the other with rest of the population. Classification rule learning is applied to extract rules to explain why certain class of behavior occurs. We present a rule learning algorithm that analyzes test measurement data in terms of design features to generate rules, and conduct controlled experiments to demonstrate the effectiveness of the proposed approach. Results show that the proposed learning approach can effectively uncover rules responsible for the designsilicon mismatch even when significant noises are associated with both the measurement data and the class partitioning results for capturing the unexpected behavior.