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A Survey and Recent Advances: Machine Intelligence in Electronic Testing

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Abstract

Integrated circuit (IC) testing presents complex problems that for large circuits are exceptionally difficult to solve by traditional computing techniques. To deal with unmanageable time complexity, engineers often rely on human “hunches" and “heuristics" learned through experience. Training computers to adopt these human skills is referred to as machine intelligence (MI) or machine learning (ML). This survey examines applications of such methods to test analog, radio frequency (RF), digital, and memory circuits. It also summarizes ML applications to hardware security and emerging technologies, highlighting challenges and potential research directions. The present work is an extension of a recent paper from IEEE VLSI Test Symposium (VTS’21), and includes recent applications of artificial neural network (ANN) and principal component analysis (PCA) to automatic test pattern generation (ATPG).

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  1. Intel Corp., Santa Clara, CA, 95054, USA

    Soham Roy

  2. Dynetics Inc., Huntsville, AL, 35806, USA

    Spencer K. Millican

  3. Auburn Univ., Auburn, AL, 36849, USA

    Vishwani D. Agrawal

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  1. Soham Roy
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Roy, S., Millican, S.K. & Agrawal, V.D. A Survey and Recent Advances: Machine Intelligence in Electronic Testing. J Electron Test (2024). https://doi.org/10.1007/s10836-024-06117-7

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