The nature of statistical learning theory
The nature of statistical learning theory
Test structures for delay variability
Proceedings of the 8th ACM/IEEE international workshop on Timing issues in the specification and synthesis of digital systems
Debug methodology for the McKinley processor
Proceedings of the IEEE International Test Conference 2001
Fault diagnosis based on effect-cause analysis: An introduction
DAC '80 Proceedings of the 17th Design Automation Conference
Delay Defect Diagnosis Based Upon Statistical Timing Models " The First Step
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Design-silicon timing correlation: a data mining perspective
Proceedings of the 44th annual Design Automation Conference
Silicon speedpath measurement and feedback into EDA flows
Proceedings of the 44th annual Design Automation Conference
Statistical framework for technology-model-product co-design and convergence
Proceedings of the 44th annual Design Automation Conference
Path selection for monitoring unexpected systematic timing effects
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Speedpath analysis based on hypothesis pruning and ranking
Proceedings of the 46th Annual Design Automation Conference
Proceedings of the 47th Design Automation Conference
Classifying circuit performance using active-learning guided support vector machines
Proceedings of the International Conference on Computer-Aided Design
Data mining in design and test processes: basic principles and promises
Proceedings of the 2013 ACM international symposium on International symposium on physical design
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Explaining the mismatch between predicted timing behavior from modeling and simulation, and the observed timing behavior measured on silicon chips can be very challenging. Given a list of potential sources, the mismatch can be the aggregate result caused by some of them both individually and collectively, resulting in a very large search space. Furthermore, observed data are always corrupted by some unknown statistical random noises. To overcome both challenges, this paper proposes a statistical diagnosis framework that formulates the diagnosis problem as a regression learning problem. In this diagnosis framework, the objective is to rank a set of features corresponding to the list of potential sources of concern. The rank is based on measured silicon path delay data such that a feature inducing a larger unexpected timing deviation is ranked higher. Experimental results are presented to explain the learning method. Diagnosis effectiveness will be demonstrated through benchmark experiments and on an industrial design.