Optimizing Models Based OPC fragmentation using genetic algorithms

Abstract

Models Based Optical Proximity Correction (MBOPC) is used extensively in the semiconductor industry to achieve robust pattern fidelity in modern lithographic processes. Much of the complexity in OPC algorithms is handled by advanced commercial software packages. These packages give users the ability to set many parameters in the OPC code decks which are used to customize the recipes for specific design styles and manufacturing process settings. Some of the most important parameters in traditional OPC recipes are the fragmentation rules, which determine how edges of polygons are fragmented in a traditional edge-based correction algorithm. It is important to find settings which can deliver good results on a wide variety of complex layout styles. One approach to setting these parameters is through a Design of Experiments (DOE) approach where many different settings are tested in a systematic fashion, in an attempt to find appropriate fragmentation rules for a wide variety of layouts. This is a very straight-forward and powerful technique, but it can be very computationally expensive, particularly as the number of independent variables becomes large. In this paper we examine the usefulness of Genetic Algorithm (GA) optimization techniques for setting the fragmentation parameters. Our work is focused on using GAs to tune parameters rather than on core algorithms used in mask data correction. We use challenging metal layout patterns and optimize fragmentation rules to try to minimize residual edge placement errors, while trying to generate fragmentation that does not result in excessive runtime, or mask manufacturing challenges. © 2008 SPIE.