Optimizing Models Based OPC Fragmentation using GeneticAlgorithms

摘要

Models Based Optical Proximity Correction (MBOPC) is used extensively in the semiconductor industry to achieverobust pattern fidelity in modern lithographic processes. Much of the complexity in OPC algorithms is handled byadvanced commercial software packages. These packages give users the ability to set many parameters in the OPC codedecks which are used to customize the recipes for specific design styles and manufacturing process settings. Some of themost important parameters in traditional OPC recipes are the fragmentation rules, which determine how edges ofpolygons are fragmented in a traditional edge-based correction algorithm. It is important to find settings which candeliver 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 differentsettings are tested in a systematic fashion, in an attempt to find appropriate fragmentation rules for a wide variety oflayouts. 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 GeneticAlgorithm (GA) optimization techniques for setting the fragmentation parameters. Our work is focused on using GAs totune parameters rather than on core algorithms used in mask data correction. We use challenging metal layout patternsand optimize fragmentation rules to try to minimize residual edge placement errors, while trying to generatefragmentation that does not result in excessive runtime, or mask manufacturing challenges.