@inproceedings{1c06955fc5964697a05d37177da92e9d,
title = "Design Automation of CMOS Op-Amps Using Statistical Geometric Programming",
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.",
keywords = "CMOS Op-Amps, Design Automation, Geometric Programming, Linear Regression, Statistical GP",
author = "Chowdhury, {Sangjukta R.} and Sumit Bhardwaj and Jennifer Kitchen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 ; Conference date: 27-05-2022 Through 01-06-2022",
year = "2022",
doi = "10.1109/ISCAS48785.2022.9937871",
language = "English (US)",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1575--1579",
booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2022",
}