Hierarchical statistical analysis of performance variation for continuous-time delta-sigma modulators

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

4 Scopus citations

Abstract

Statistical analysis has become increasingly important with increasing process parameter variations in manufacturing. Monte Carlo method has been most popular for statistical analysis, but it is not efficient for complex circuits/systems due to overwhelming computational time. In this paper, we present a general hierarchical method for efficient statistical analysis of performance parameter variations for complex circuits/systems and conduct a case study on a 4th order continuous-time Delta Sigma modulator. At circuit-level, we use response surface modeling method to extract quadratic models of circuit-level performance parameters in terms of process parameter variations. Then, at system-level, we use behavioral models to extract statistical distribution of the overall system performance parameter. The method can achieve a good tradeoff between computational efficiency and accuracy.

Original languageEnglish (US)
Title of host publication2007 IFIP International Conference on Very Large Scale Integration, VLSI-SoC
Pages37-41
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event2007 IFIP International Conference on Very Large Scale Integration, VLSI-SoC - Atlanta, GA, United States
Duration: Oct 15 2007Oct 17 2007

Publication series

Name2007 IFIP International Conference on Very Large Scale Integration, VLSI-SoC

Other

Other2007 IFIP International Conference on Very Large Scale Integration, VLSI-SoC
CountryUnited States
CityAtlanta, GA
Period10/15/0710/17/07

Keywords

  • Behavioral modeling
  • Delta-Sigma modulator
  • Process variation
  • Response surface modeling
  • Statistical analysis

Fingerprint Dive into the research topics of 'Hierarchical statistical analysis of performance variation for continuous-time delta-sigma modulators'. Together they form a unique fingerprint.

Cite this