Hierarchical statistical analysis of complex analog and mixed-signal systems

Matthew Webb, Hua Tang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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
Volume101
Issue number12
DOIs
StatePublished - Dec 2 2014

Keywords

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

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