Machine learning to improve accuracy of fast lithographic hotspot detection
Machine learning to improve accuracy of fast lithographic hotspot detection
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DOI：
10.1117/12.2515139
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发表时间：
2019-03
期刊：
影响因子：
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通讯作者：
Namjae Kim;Kichul Park;Jiwon Oh;Sang-Hun Jung;Sangah Lee;Jae-Hyun Kang;S. Paek;K. Madkour;Wael ElManhawy;Aliaa Kabeel;Ahmed ElGhoroury;M. Shafee;Asmaa Rabie;Joe Kwan
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文献类型：
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作者：
Namjae Kim;Kichul Park;Jiwon Oh;Sang-Hun Jung;Sangah Lee;Jae-Hyun Kang;S. Paek;K. Madkour;Wael ElManhawy;Aliaa Kabeel;Ahmed ElGhoroury;M. Shafee;Asmaa Rabie;Joe Kwan
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.
