Automated die inking: A pattern recognition-based approach

Xanthopoulos, Constantinos; Sarson, Peter; Reiter, H.; Makris, Yiorgos

doi:10.1109/test.2017.8242040

Cited by 9 publications

(2 citation statements)

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“…Burn‐in tests are also generally applied for detecting such latent defects. However, burn‐in tests are prohibitive because of the cost and the complexity involved (Xanthopoulos, Sarson, Reiter, & Makris, 2017). A method to automate this process using ML has been reported in Xanthopoulos et al (2017).…”

Section: Yield Learning and Diagnosismentioning

confidence: 99%

“…However, burn‐in tests are prohibitive because of the cost and the complexity involved (Xanthopoulos, Sarson, Reiter, & Makris, 2017). A method to automate this process using ML has been reported in Xanthopoulos et al (2017). Faulty dies, which are close to defective clusters in a wafer, are inked manually while training the model.…”

Section: Yield Learning and Diagnosismentioning

confidence: 99%

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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%

Section: Yield Learning and Diagnosismentioning

confidence: 99%