Machine Learning for Testability Prediction

Yuzhe Ma ()
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Yuzhe Ma: The Hong Kong University of Science and Technology (Guangzhou)

Chapter Chapter 6 in Machine Learning Applications in Electronic Design Automation, 2022, pp 151-180 from Springer

Abstract: Abstract VLSI testing has become a significant concern in the modern design flow, which accounts for the reliability and development cost of a modern chip design. Recent advances in machine learning provide new methodologies to enhance various design stages in the design cycle. This chapter will discuss typical machine learning approaches for testability measurements, which focuses on a set of testability-related prediction problems in both component level and circuit level. In addition, several considerations on applying machine learning models for practical testability improvement are introduced.

Keywords: Testability analysis; Machine learning; Deep learning; Design for testability; Graph neural network (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13074-8_6

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DOI: 10.1007/978-3-031-13074-8_6

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