Efficient Hybrid Performance Modeling for Analog Circuits Using Hierarchical Shrinkage Priors

Journal Article (Journal Article)

Efficient performance modeling is an extremely important task for yield analysis and design optimization of analog circuits. In this paper, a novel regression modeling method based on hierarchical shrinkage priors is proposed to construct hybrid performance models with both high accuracy and low computational cost. In particular, the user-defined model templates derived from design equations and the general-purpose orthogonal polynomials are combined together to set up a hybrid dictionary. Next, in order to avoid over-shrinking large model coefficients, a novel regression method based on hierarchical shrinkage priors and variational Bayesian inference is adopted for model fitting. A rail-to-rail operational amplifier example demonstrates that the proposed method achieves up to 40% error reduction over other state-of-the-art approaches without increasing the modeling cost.

Full Text

Duke Authors

Cited Authors

  • Liao, C; Tao, J; Yu, H; Tang, Z; Su, Y; Zhou, D; Zeng, X; Li, X

Published Date

  • December 1, 2016

Published In

Volume / Issue

  • 35 / 12

Start / End Page

  • 2148 - 2152

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2016.2543021

Citation Source

  • Scopus