Special session: Machine learning for semiconductor test and reliability

Hussam Amrouch, Animesh Basak Chowdhury, Wentian Jin, Ramesh Karri, Farshad Khorrami, Prashanth Krishnamurthy, Ilia Polian, Victor M. Van Santen, Benjamin Tan, Sheldon X.D. Tan

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

8 Scopus citations

Abstract

With technology scaling approaching atomic levels, IC test and diagnosis of complex System-on-Chips (SoCs) become overwhelming challenging. In addition, sustaining the reliability of transistors as well as circuits at such extreme feature sizes, for the entire projected lifetime, also become profoundly difficult. This holds even more when it comes to emerging technologies that go beyond convectional CMOS in which the underlying physics are not yet fully understood. In this special session paper, we describe the usage of machine learning in several test and reliability related areas. First, we demonstrate the vital role that machine learning can play in IC test showing the importance of explainability as a frontier for machine learning in IC test. Afterwards, we discuss how novel physics-informed neural networks can be employed to model electrostatic problems in VLSI designs. This is essential to mitigate the deleterious effects of of time dependent dielectric breakdown, which is the key source of reliability degradations. Finally, we discuss the major sources of reliability degradations at the transistor level in advanced technology nodes such as transistor aging phenomena and self-heating effects as well as we demonstrate how machine learning approaches can further help in developing reliable emerging technologies.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 39th VLSI Test Symposium, VTS 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665419499
DOIs
StatePublished - 25 Apr 2021
Externally publishedYes
Event39th IEEE VLSI Test Symposium, VTS 2021 - San Diego, United States
Duration: 26 Apr 202128 Apr 2021

Publication series

NameProceedings of the IEEE VLSI Test Symposium
Volume2021-April

Conference

Conference39th IEEE VLSI Test Symposium, VTS 2021
Country/TerritoryUnited States
CitySan Diego
Period26/04/2128/04/21

Keywords

  • BTI
  • Electromigration
  • Emerging Technology
  • HCI
  • IC Test
  • Machine learning
  • Reliability

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