24 August 2017 Imbalance aware lithography hotspot detection: a deep learning approach
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Abstract
With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Haoyu Yang, Haoyu Yang, Luyang Luo, Luyang Luo, Jing Su, Jing Su, Chenxi Lin, Chenxi Lin, Bei Yu, Bei Yu, } "Imbalance aware lithography hotspot detection: a deep learning approach," Journal of Micro/Nanolithography, MEMS, and MOEMS 16(3), 033504 (24 August 2017). https://doi.org/10.1117/1.JMM.16.3.033504 . Submission: Received: 10 May 2017; Accepted: 1 August 2017
Received: 10 May 2017; Accepted: 1 August 2017; Published: 24 August 2017
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