16 October 2017 Machine learning for mask/wafer hotspot detection and mask synthesis
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Proceedings Volume 10451, Photomask Technology; 104510A (2017); doi: 10.1117/12.2282943
Event: SPIE Photomask Technology and EUV Lithography, 2017, Monterey, California, United States
Abstract
Machine learning is a powerful computer science technique that can derive knowledge from big data and make predictions/decisions. Since nanometer integrated circuits (IC) and manufacturing have extremely high complexity and gigantic data, there is great opportunity to apply and adapt various machine learning techniques in IC physical design and verification. This paper will first give an introduction to machine learning, and then discuss several applications, including mask/wafer hotspot detection, and machine learning-based optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion. We will further discuss some challenges and research directions.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yibo Lin, Xiaoqing Xu, Jiaojiao Ou, David Z. Pan, "Machine learning for mask/wafer hotspot detection and mask synthesis", Proc. SPIE 10451, Photomask Technology, 104510A (16 October 2017); doi: 10.1117/12.2282943; http://dx.doi.org/10.1117/12.2282943
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KEYWORDS
Machine learning

Optical proximity correction

SRAF

Feature extraction

Model-based design

Photomasks

Lithography

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