Improving hyper-NA OPC using targeted measurements for model parameter extraction

Abstract

An alternative method of OPC model fitting based on model parameter sensitivity is presented. Theoretical advantages are discussed, including improved model quality and time to results. The parameter sensitivity method is applied using a basic optical model to 32nm logic node experimental data. Results include standard and parameter sensitivity model fits using both constant and variable threshold models. The results show that the parameter sensitivity methodology enables an overall model fit that is more physically-predictive than a standard OPC model fit.