Design Automation of CMOS Op-Amps Using Statistical Geometric Programming

Sangjukta R. Chowdhury, Sumit Bhardwaj, Jennifer Kitchen

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

5 Scopus citations

Abstract

This work proposes a novel design automation (DA) technique that uses a multifaceted approach combining Multivariate Regression with Geometric Programming (GP) to design analog circuits. Previous DA methods employing GP have typically used analytical derivations of the various design equations representing an analog circuit. The proposed DA method eliminates the need for analytical derivations by using simulation data and multivariate regression to generate statistical models combined with GP to solve these statistical expressions with respect to optimum circuit design parameters. This presented statistical GP method has been applied to successfully design a five-transistor two-stage operational amplifier and a folded cascode amplifier in a TSMC 65nm CMOS technology. The presented statistical GP DA results are comparable to the design results obtained from both analytical GP and manual design by an experienced analog design engineer.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Circuits and Systems, ISCAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1575-1579
Number of pages5
ISBN (Electronic)9781665484855
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
Duration: May 27 2022Jun 1 2022

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2022-May

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/TerritoryUnited States
CityAustin
Period5/27/226/1/22

Keywords

  • CMOS Op-Amps
  • Design Automation
  • Geometric Programming
  • Linear Regression
  • Statistical GP

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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