Statistical reliability analysis under process variation and aging effects

Lu Yinghai, Shang Li, Zhou Hai, Zhu Hengliang, Yang Fan, Zeng Xuan*

*Corresponding author for this work

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

75 Scopus citations


Circuit reliability is affected by various fabrication-time and run-time effects. Fabrication-induced process variation has significant impact on circuit performance and reliability. Various aging effects, such as negative bias temperature instability, cause continuous performance and reliability degradation during circuit run-time usage. In this work, we present a statistical analysis framework that characterizes the lifetime reliability of nanometer-scale integrated circuits by jointly considering the impact of fabrication-induced process variation and run-time aging effects. More specifically, our work focuses on characterizing circuit threshold voltage lifetime variation and its impact on circuit timing due to process variation and the negative bias temperature instability effect, a primary aging effect in nanometer-scale integrated circuits. The proposed work is capable of characterizing the overall circuit lifetime reliability, as well as efficiently quantifying the vulnerabilities of individual circuit elements. This analysis framework has been carefully validated and integrated into an iterative design flow for circuit lifetime reliability analysis and optimization.

Original languageEnglish (US)
Title of host publication2009 46th ACM/IEEE Design Automation Conference, DAC 2009
Number of pages6
StatePublished - 2009
Event2009 46th ACM/IEEE Design Automation Conference, DAC 2009 - San Francisco, CA, United States
Duration: Jul 26 2009Jul 31 2009

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Other2009 46th ACM/IEEE Design Automation Conference, DAC 2009
Country/TerritoryUnited States
CitySan Francisco, CA


  • NBTI
  • Process variations
  • Yield

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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