Patent · US Active

Evaluating functional fault criticality of structural faults for circuit testing

US12008298B2 · kind B2 · utility

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0References

12Claims

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Assignee

Duke University · US

Inventors

Krishnendu Chakrabarty · Chapel Hill, US
Arjun Chaudhuri · Durham, US
Jonti Talukdar · Durham, US

Key dates

Filing date	Oct 26, 2020
Grant date	Jun 11, 2024
Priority date	—
Expiry date	Apr 13, 2043

Classification

Technology area (CPC G)Physics
CPC primaryG06N3/10
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

A system for evaluating fault criticality using machine learning includes a first machine learning module that is trained on a subset of a circuit and used for evaluating whether a node in a netlist of the entire circuit is a critical node, and a second machine learning module specialized to minimize classification errors in nodes predicted as benign. A generative adversarial network can be used to generate synthetic test escape data to supplement data used to train the second machine learning module.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.