Compact OPC model optimization using emulated data

Abstract

In this work compact optical proximity correction (OPC) model optimization methodology is presented. The methodology requires less measured empirical (wafer) metrology data for model calibration than conventional approaches, but still enables successful compact OPC model building which can be extrapolated to various process conditions within a focus-exposure matrix (FEM). In order to ensure compact modeling success, a rigorous modeling technique is incorporated in the modeling flow for generation of additional emulated data. The emulated data, along with original empirical data, are used in the compact OPC model optimization process. The presented methodology couples rigorous and compact modeling, to reduce both need of large quantities of empirical data collected from test wafers, and metrology noise impact on model calibration processes, and as well as increases accurate and predictable compact models throughput. Initial tests have shown that by using 5x less empirical data, the presented methodology results a compact OPC model which is in excellent agreement with a model that had been calibrated using the full empirical data set.