Improvements in accuracy of dense OPC models

Abstract

ABSTRACT Performing model-based optical proximity correction (MBOPC) on layouts has become an integral part of patterning advanced integrated circuits. Earlier technologies used sparse OPC, the run times of which explode when the density of layouts increases. With the move to 45 nm technology node, this increase in run time has resulted in a shift to dense simulation OPC, which is pixel-based. The dense approach becomes more efficient at 45nm technology node and beyond. New OPC model forms can be used with the dense simulation OPC engine, providing the greater accuracy required by smaller technology nodes. Parameters in the optical model have to be optimized to achieve the required accuracy. Dense OPC uses a resist model with a different set of parameters than sparse OPC. The default search ranges used in the optimization of these resist parameters do not always result in the best accuracy. However, it is possible to improve the accuracy of the resist models by understanding th e restrictions placed on the search ranges of the physical parameters during optimization. This paper will present resu lts showing the correlation between accuracy of the models and some of these optical and resist parameters. The results will show that better optimization can improve the model fitness of features in both the calibration and verification set. Keyword List: Dense OPC model, Source Map, Focus Blur, Resist optimization, Diffusion distance, Base concentration