By Topic

Parameter extraction for statistical IC modeling based on recursive inverse approximation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ming Qu ; Nat. Semicond. Corp., Santa Clara, CA, USA ; Styblinski, M.A.

An accurate and efficient parameter extraction methodology, utilizing a new technique called recursive inverse approximation (RIA), is proposed for statistical modeling of integrated circuits. The main features of RIA are (1) linear approximation is used to obtain initial model parameter estimates, (2) reverse verification performs accuracy checking, and (3) error correction functions are constructed in the extracted parameter space to recursively refine the previously extracted parameter values. As a result, an approximate inverse mapping from the measured performance space to the model parameter space is established for statistical parameter extraction. Examples of parameter extraction for MOS transistors and IC multiplier block demonstrate high efficiency and accuracy of the new method

Published in:

Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on  (Volume:16 ,  Issue: 11 )