Machine Learning for Detection of Competing Wearout Mechanisms

Shu Han Hsu, Kexin Yang, Linda Milor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Because data from a variety of wearout mechanisms is confounded in circuits, we apply machine learning techniques to detect the parameters of competing failure mechanisms in ring oscillators, which more closely mimic circuit behavior than test structures. This is the first known application using data analysis to distinguish competing wearout mechanisms in circuit-level data. To quickly and efficiently analyze failure data, we propose to use maximum likelihood estimation to separately determine the parameters of each underlying distribution by only observing the time-to-failure of samples. The quasi-Newton method is used to update and optimize the parameter extraction.

Original languageEnglish
Title of host publication2019 IEEE International Reliability Physics Symposium, IRPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538695043
DOIs
Publication statusPublished - 2019 May 22
Event2019 IEEE International Reliability Physics Symposium, IRPS 2019 - Monterey, United States
Duration: 2019 Mar 312019 Apr 4

Publication series

NameIEEE International Reliability Physics Symposium Proceedings
Volume2019-March
ISSN (Print)1541-7026

Conference

Conference2019 IEEE International Reliability Physics Symposium, IRPS 2019
Country/TerritoryUnited States
CityMonterey
Period19-03-3119-04-04

All Science Journal Classification (ASJC) codes

  • General Engineering

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