Highly accurate modeling by using 2-dimensional calibration data set for model-based OPC verification

Abstract

As the k1 factor and minimum feature sizes decrease, the use of optical proximity correction (OPC) is increasing and is getting more complex. The complexity increases the possibility of correction errors like improper placement of edges in the OPC output data such that the printed results will deviate from target design. In this paper we will describe new modeling method by using 2-dimensional test structures for model based verification of post OPC data. Recently, most of the semiconductor companies implement a system for model based verification (MBV) for post OPC data into a manufacturing data flow. In case of model based verification, the most important thing is the accuracy of model which is used to detect the potential hot spot and critical errors like pinching-bridging errors and CD variation. For good model accuracy, process change has to be feedback to the model generation step by injecting real wafer information. Therefore, optimization process of 2-dimensional data set is needed. We proposed new modeling method by using optimization process of calibration data set which consists of 2-dimensional structures. Also, we present results of MBV and discuss about constraints and considerations of model based verification.