Parameter extraction for statistical IC modeling based on recursive inverse approximation

Abstract

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.