The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
Machine Learning
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Debug methodology for the McKinley processor
Proceedings of the IEEE International Test Conference 2001
Architecting ASIC libraries and flows in nanometer era
Proceedings of the 40th annual Design Automation Conference
Fault diagnosis based on effect-cause analysis: An introduction
DAC '80 Proceedings of the 17th Design Automation Conference
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Statistical timing analysis based on a timing yield model
Proceedings of the 41st annual 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
BEOL variability and impact on RC extraction
Proceedings of the 42nd annual Design Automation Conference
Process and environmental variation impacts on ASIC timing
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Proceedings of the conference on Design, automation and test in Europe: Proceedings
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
Speedpath prediction based on learning from a small set of examples
Proceedings of the 45th annual Design Automation Conference
A "true" electrical cell model for timing, noise, and power grid verification
Proceedings of the 45th annual Design Automation Conference
Speedpath analysis based on hypothesis pruning and ranking
Proceedings of the 46th Annual Design Automation Conference
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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Traditional diagnosis of defects is based on an assumed fault model. A failing chip is diagnosed to find the subset of faults that can best explain the failure. This paper illustrates a link between this traditional perspective of diagnosis and a new perspective where diagnosis is seen as a form of data learning. We explain that both defect diagnosis and data learning are solving so-called ill-posed problems and the technique for solving such a problem is called regularization. We illustrate a diagnosis framework that employs various data learning techniques to implement two diagnosis approaches: feature ranking and rule extraction. This diagnosis framework is designed to uncover design-related issues that cause systematic uncertainties or any unexpected behavior in silicon. We review the work that has been accomplished for implementing this framework and further discuss issues with its practical application.