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25 October 2016 The performance improvement of SRAF placement rules using GA optimization
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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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yan Xu, Bidan Zhang, Changan Wang, William Wilkinson, and John Bolton "The performance improvement of SRAF placement rules using GA optimization", Proc. SPIE 9985, Photomask Technology 2016, 99851C (25 October 2016);


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