Machine Learning based Performance Prediction of Microcontrollers using Speed Monitors

Riccardo Cantoro, Martin Huch, Tobias Kilian, Raffaele Martone, Ulf Schlichtmann, Giovanni Squillero

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

19 Scopus citations

Abstract

During the manufacturing process, electronic devices are thoroughly tested for defects. However, testing for well-known fault models, such as stuck-At and transition delay, may not be sufficient for an effective performance screening. In modern devices, Design-for-Testability features embedded at design time can allow the tester to apply stimuli and measure different critical parameters. We propose to use some of these structures, namely the speed monitors, to predict the maximum operating speed, and screen out under-performing devices. We design a complete methodology, from the extraction of robust labels, through a machine-learning algorithm, down to a post-processing step, able to meet the quality standards imposed by industry. Experimental results using real production data demonstrate the feasibility of the approach.

Original languageEnglish
Title of host publication2020 IEEE International Test Conference, ITC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728191133
DOIs
StatePublished - 1 Nov 2020
Event2020 IEEE International Test Conference, ITC 2020 - Washington, United States
Duration: 1 Nov 20206 Nov 2020

Publication series

NameProceedings - International Test Conference
Volume2020-November
ISSN (Print)1089-3539

Conference

Conference2020 IEEE International Test Conference, ITC 2020
Country/TerritoryUnited States
CityWashington
Period1/11/206/11/20

Keywords

  • Machine Learning
  • Performance Screening
  • Speed Monitors

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