Systematic defect detection methodology for volume diagnosis: A data mining perspective

Shan, Chuanhe Jay; Babighian, Pietro; Pan, Yan; Carulli, John M.; Wang, Li-C.

doi:10.1109/test.2017.8242050

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…A method to assist PFA by narrowing down the possible set of defects is discussed in Shan, Babighian, Pan, Carulli, and Wang (2017). A defect can have various signatures called “defective modes.” During volume diagnosis, χ 2 independence test is applied to check whether the defects and the “defective modes” are related.…”

Section: Yield Learning and Diagnosismentioning

confidence: 99%

A survey of digital circuit testing in the light of machine learning

Pradhan

Bhattacharya

2020

WIREs Data Min & Knowl

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The insistent trend in today's nanoscale technology, to keep abreast of the Moore's law, has been continually opening up newer challenges to circuit designers. With rapid downscaling of integration, the intricacies involved in the manufacturing process have escalated significantly. Concomitantly, the nature of defects in silicon chips has become more complex and unpredictable, adding further difficulty in circuit testing and diagnosis. The volume of test data has surged and the parameters that govern testing of integrated circuits have increased not only in dimension but also in the complexity of their correlation. Evidently, the current scenario serves as a pertinent platform to explore new test solutions based on machine learning. In this survey, we look at various recent advances in this evolving domain in the context of digital logic testing and diagnosis. This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Machine Learning Technologies > Prediction

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Section: Yield Learning and Diagnosismentioning

confidence: 99%