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CAPP: context analyzer and printability predictor
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Paper Abstract

Layout context plays a very significant role in printability of layout shapes, and hence it is extremely critical to include layout context information while performing printability checks. In this paper, we are proposing a unique approach of analyzing layout context geometries and use Machine Learning (ML) technique to predict lithography hotspots. Our method uses past lithography simulation results to evaluate geometry margins and profile them in simple geometry rules. The markers of these rules then analyzed by our unique context analyzer and generate data set for train Arterial Neural Network (ANN). Later this trained ANN model used for predictions on new input designs. In this paper, we will also present results to highlight how our approach is better in the accuracy of lithography hotspots detection in comparison to previous work related to pattern matching and machine-learning techniques.

Paper Details

Date Published: 20 March 2019
PDF: 13 pages
Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620R (20 March 2019); doi: 10.1117/12.2513655
Show Author Affiliations
Vikas Tripathi, GLOBALFOUNDRIES Singapore Pte. Ltd. (Singapore)
Yongfu Li, GLOBALFOUNDRIES Singapore Pte. Ltd. (Singapore)
I-Lun Tseng, GLOBALFOUNDRIES Singapore Pte. Ltd. (Singapore)
Zhao Chuan Lee, GLOBALFOUNDRIES Singapore Pte. Ltd. (Singapore)
Valerio Perez, GLOBALFOUNDRIES Singapore Pte. Ltd. (Singapore)
Jonathan Ong, GLOBALFOUNDRIES Singapore Pte. Ltd. (Singapore)
Shobhit Malik, GLOBALFOUNDRIES Inc. (United States)


Published in SPIE Proceedings Vol. 10962:
Design-Process-Technology Co-optimization for Manufacturability XIII
Jason P. Cain, Editor(s)

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