Aging-aware chip health prediction adopting an innovative monitoring strategy

Yun Ting Wang, Kai-Chiang Wu, Chung Han Chou, Shih Chieh Chang

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

1 Scopus citations

Abstract

Concerns exist that the reliability of chips is worsening because of downscaling technology. Among various reliability challenges, device aging is a dominant concern because it degrades circuit performance over time. Traditionally, runtime monitoring approaches are proposed to estimate aging effects. However, such techniques tend to predict and monitor delay degradation status for circuit mitigation measures rather than the health condition of the chip. In this paper, we propose an aging-aware chip health prediction methodology that adapts to workload conditions and process, supply voltage, and temperature variations. Our prediction methodology adopts an innovative on-chip delay monitoring strategy by tracing representative aging-aware delay behavior. The delay behavior is then fed into a machine learning engine to predict the age of the tested chips. Experimental results indicate that our strategy can obtain 97.40% accuracy with 4.14% area overhead on average. To the authors' knowledge, this is the first method that accurately predicts current chip age and provides information regarding future chip health.

Original languageEnglish
Title of host publicationASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages179-184
Number of pages6
ISBN (Electronic)9781450360074
DOIs
StatePublished - 21 Jan 2019
Event24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 - Tokyo, Japan
Duration: 21 Jan 201924 Jan 2019

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
CountryJapan
CityTokyo
Period21/01/1924/01/19

Keywords

  • Aging
  • Bias-temperature instability
  • Chip health prediction
  • Process
  • Support vector machine (SVM)
  • Temperature (PVT) variation
  • Voltage
  • Workload

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  • Cite this

    Wang, Y. T., Wu, K-C., Chou, C. H., & Chang, S. C. (2019). Aging-aware chip health prediction adopting an innovative monitoring strategy. In ASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference (pp. 179-184). (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3287624.3287687