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Proceedings Paper

Machine learning to improve accuracy of fast lithographic hotspot detection
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Paper Abstract

As the typical litho hotspot detection runtime continue to increase with sub-10nm technology node due to increasing design and process complexity, many DFM techniques are exploring new methods that can expedite some of their advanced verification processes. The benefit of improved runtimes through simulation can be obtained by reducing the amount of data being sent to simulation. By inserting a pattern matching operation, a system can be designed such that it only simulates in the vicinity of topologies that somewhat resemble hotspots while ignoring all other data. Pattern Matching improved overall runtime significantly. However, pattern matching techniques require a library of accumulated known litho hotspots in allowed accuracy rate. In this paper, we present a fast and accurate litho hotspot detection methodology using specialized machine learning. We built a deep neural network with training from real hotspot candidates. Experimental results demonstrate Machine Learning’s ability to predict hotspots and achieve greater than 90% detection accuracy and coverage, with best achieved accuracy 99.9% while reducing overall runtime compared to full litho simulation.

Paper Details

Date Published: 20 March 2019
PDF: 8 pages
Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 1096216 (20 March 2019); doi: 10.1117/12.2515139
Show Author Affiliations
NamJae Kim, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Kiheung Park, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Jiwon Oh, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Sangwoo Jung, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Sangah Lee, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Jae-hyun Kang, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Seung Weon Paek, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Kareem Madkour, Mentor Graphics Egypt (Egypt)
Wael ElManhawy, Mentor Graphics Corp. (United States)
Aliaa Kabeel, Mentor Graphics Egypt (Egypt)
Ahmed ElGhoroury, Mentor Graphics Egypt (Egypt)
Marwa Shafee, Mentor Graphics Egypt (Egypt)
Asmaa Rabie, Mentor Graphics Egypt (Egypt)
Joe Kwan, Mentor Graphics Corp. (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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