Machine learning to improve accuracy of fast lithographic hotspot detection

NamJae Kim; Kiheung Park; Jiwon Oh; Sangwoo Jung; Sangah Lee; Jae-hyun Kang; Seung Weon Paek; Kareem Madkour; Wael ElManhawy; Aliaa Kabeel; Ahmed ElGhoroury; Marwa Shafee; Asmaa Rabie; Joe Kwan

doi:10.1117/12.2515139

20 March 2019 Machine learning to improve accuracy of fast lithographic hotspot detection

NamJae Kim, Kiheung Park, Jiwon Oh, Sangwoo Jung, Sangah Lee, Jae-hyun Kang, Seung Weon Paek, Kareem Madkour, Wael ElManhawy, Aliaa Kabeel, Ahmed ElGhoroury, Marwa Shafee, Asmaa Rabie, Joe Kwan

Author Affiliations +

Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 1096216 (2019) https://doi.org/10.1117/12.2515139
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

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.

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NamJae Kim, Kiheung Park, Jiwon Oh, Sangwoo Jung, Sangah Lee, Jae-hyun Kang, Seung Weon Paek, Kareem Madkour, Wael ElManhawy, Aliaa Kabeel, Ahmed ElGhoroury, Marwa Shafee, Asmaa Rabie, and Joe Kwan "Machine learning to improve accuracy of fast lithographic hotspot detection", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 1096216 (20 March 2019); https://doi.org/10.1117/12.2515139

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KEYWORDS

Machine learning

Data modeling

Lithography

Neural networks

Computer simulations

Manufacturing

Design for manufacturability

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