Hierarchical statistical analysis of complex analog and mixed-signal systems

Matthew Webb, Hua Tang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


With increasing process parameter variations in nanometre regime, circuits and systems encounter significant performance variations and therefore statistical analysis has become increasingly important. For complex analog and mixed-signal circuits and systems, efficient yet accurate statistical analysis has been a challenge mainly due to significant simulation and modelling time. In the past years, there have been various approaches proposed for statistical analysis of analog and mixed-signal circuits. A recent work is reported to address statistical analysis for continuous-time Delta-Sigma modulators. In this article, we generalise that method and present a hierarchical method for efficient statistical analysis of complex analog and mixed-signal circuits while maintaining reasonable accuracy. At circuit level, we use the response surface modelling method to extract quadratic models of circuit-level performance parameters in terms of process parameters. Then at system level, we use behavioural models and apply the Monte-Carlo method for statistical evaluation of system performance parameters. We illustrate and validate the method on a continuous-time Delta-Sigma modulator and an analog filter.

Original languageEnglish (US)
Pages (from-to)1647-1661
Number of pages15
JournalInternational Journal of Electronics
Issue number12
StatePublished - Dec 2 2014

Bibliographical note

Publisher Copyright:
© 2014 © 2014 Taylor & Francis.


  • Hierarchical
  • Performance parameter variation
  • analog and mixed-signal systems
  • process parameter variation
  • statistical analysis


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