Machine Learning for Test, Diagnosis, Post-Silicon Validation and Yield Optimization

Hussam Amrouch, Krishnendu Chakrabarty, Dirk Pfluger, Ilia Polian, Matthias Sauer, Matteo Sonza Reorda

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


Recent breakthroughs in machine learning (ML) technology are shifting the boundaries of what is technologically possible in several areas of Computer Science and Engineering. This paper discusses ML in the context of test-related activities, including fault diagnosis, post-silicon validation and yield optimization. ML is by now an established scientific discipline, and a large number of successful ML techniques have been developed over the years. This paper focuses on how to adapt ML approaches that were originally developed with other applications in mind to test-related problems. We consider two specific applications of learning in more depth: delay fault diagnosis in three-dimensional integrated circuits and tuning performed during post-silicon validation. Moreover, we examine the emerging concept of brain-inspired hyperdimensional computing (HDC) and its potential for addressing test and reliability questions. Finally, we show how to integrate ML into actual industrial test and yield-optimization flows.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE European Test Symposium, ETS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665467063
StatePublished - 2022
Externally publishedYes
Event27th IEEE European Test Symposium, ETS 2022 - Barcelona, Spain
Duration: 23 May 202227 May 2022

Publication series

NameProceedings of the European Test Workshop
ISSN (Print)1530-1877
ISSN (Electronic)1558-1780


Conference27th IEEE European Test Symposium, ETS 2022


Dive into the research topics of 'Machine Learning for Test, Diagnosis, Post-Silicon Validation and Yield Optimization'. Together they form a unique fingerprint.

Cite this