The performance improvement of SRAF placement rules using GA optimization

Abstract

In this paper, genetic algorithm (GA) method is applied to both positive and negative Sub Resolution Assist Features (SRAF) insertion rules. Simulation results and wafer data demonstrated that the optimized SRAF rules helped resolve the SRAF printing issues while dramatically improving the process window of the working layer. To find out the best practice to place the SRAF, model-based SRAF (MBSRAF), rule-based SRAF (RBSRAF) with pixelated OPC simulation and RBSRAF with GA method are thoroughly compared. The result shows the apparent advantage of RBSRAF with GA method.