IP Session on Machine Learning Applications in IC Test-Related Tasks

Ghada Sokar, Yassien Zakaria, Asmaa Rabie, Kareem Madkour, Ira Leventhal, Jochen Rivoir, Xinli Gu, Haralampos G. Stratigopoulos

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    Over the last decade there has been a surge of activity in employing advanced statistical analysis and machine learning methods to various test-related tasks. The topic is no longer simply a matter of academic curiosity but, rather, a pressing need of the industry as it seeks to address various challenges. In this session, three industry experts have been invited to give their perspective, describe machine learning use cases, and discuss challenges and future work ideas. The three talks will cover the use of deep learning for hotspot detection, the challenge of rendering machine learning-based decisions in the semiconductor industry trustable and explainable, and data analytics across the the complete product cycle towards improved product reliability.

    Original languageEnglish
    Title of host publication2019 IEEE 37th VLSI Test Symposium, VTS 2019
    PublisherIEEE Computer Society
    Number of pages1
    ISBN (Electronic)9781728111704
    Publication statusPublished - Apr 2019
    Event37th IEEE VLSI Test Symposium, VTS 2019 - Monterey, United States
    Duration: 23 Apr 201925 Apr 2019


    Conference37th IEEE VLSI Test Symposium, VTS 2019
    CountryUnited States

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