CAPP: context analyzer and printability predictor

Vikas Tripathi; Yongfu Li; I-Lun Tseng; Zhao Chuan Lee; Valerio Perez; Jonathan Ong; Shobhit Malik

doi:10.1117/12.2513655

20 March 2019 CAPP: context analyzer and printability predictor

Vikas Tripathi, Yongfu Li, I-Lun Tseng, Zhao Chuan Lee, Valerio Perez, Jonathan Ong, Shobhit Malik

Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620R (2019) https://doi.org/10.1117/12.2513655
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

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.

Conference Presentation

Citation Download Citation

Vikas Tripathi, Yongfu Li, I-Lun Tseng, Zhao Chuan Lee, Valerio Perez, Jonathan Ong, and Shobhit Malik "CAPP: context analyzer and printability predictor", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620R (20 March 2019); https://doi.org/10.1117/12.2513655

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