An accurate PLL behavioral model for fast monte carlo analysis under process variation

Chin Cheng Kuo*, Meng Jung Lee, I. Ching Tsai, Chien-Nan Liu, Ching Ji Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Hierarchical statistical analysis using the regression-based approach is often used to improve the extremely expensive HSPICE Monte Carlo (MC) analysis. However, accurately fitting the regression equations requires many simulation samples. In this paper, an accurate Behavioral Monte Carlo Simulation (BMCS) approach to analyze PLL designs under process variation is developed by building a bottom-up behavioral modeling approach with an efficient extraction process. Using the accurate model, we also develop a modified sensitivity analysis for process variation effects to provide accurate enough results with less regression cost. As shown in the experimental results, we reduce the simulation time of HSPICE MC analysis from several weeks to several hours and still retain similar statistical results as in HSPICE MC simulation.

Original languageEnglish
Title of host publication2007 IEEE International Behavioral Modeling and Simulation Workshop, BMAS
Pages110-114
Number of pages5
DOIs
StatePublished - 1 Dec 2007
Event2007 IEEE International Behavioral Modeling and Simulation Workshop, BMAS - San Jose, CA, United States
Duration: 20 Sep 200721 Sep 2007

Publication series

Name2007 IEEE International Behavioral Modeling and Simulation Workshop, BMAS

Conference

Conference2007 IEEE International Behavioral Modeling and Simulation Workshop, BMAS
CountryUnited States
CitySan Jose, CA
Period20/09/0721/09/07

Fingerprint Dive into the research topics of 'An accurate PLL behavioral model for fast monte carlo analysis under process variation'. Together they form a unique fingerprint.

Cite this