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

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

Automated summary

In today's nanoscale technology, the rapid downscaling of integration has increased the complexity of the manufacturing process and defects in silicon chips, posing challenges for circuit testing and diagnosis. The surge in test data volume and the complexity of parameters governing integrated circuit testing provide a platform for exploring new test solutions based on machine learning.