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Proceedings Paper

The performance improvement of SRAF placement rules using GA optimization
Author(s): Yan Xu; Bidan Zhang; Changan Wang; William Wilkinson; John Bolton
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Paper 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.

Paper Details

Date Published: 25 October 2016
PDF: 7 pages
Proc. SPIE 9985, Photomask Technology 2016, 99851C (25 October 2016); doi: 10.1117/12.2241015
Show Author Affiliations
Yan Xu, GLOBALFOUNDRIES Inc. (United States)
Bidan Zhang, GLOBALFOUNDRIES Inc. (United States)
Changan Wang, GLOBALFOUNDRIES Inc. (United States)
William Wilkinson, GLOBALFOUNDRIES Inc. (United States)
John Bolton, GLOBALFOUNDRIES Inc. (United States)

Published in SPIE Proceedings Vol. 9985:
Photomask Technology 2016
Bryan S. Kasprowicz; Peter D. Buck, Editor(s)

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