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.
Yibo Lin, Xiaoqing Xu, Jiaojiao Ou, and David Z. Pan, "Machine learning for mask/wafer hotspot detection and mask synthesis," Proc. SPIE 10451, Photomask Technology, 104510A (Presented at SPIE Photomask Technology and EUV Lithography: September 11, 2017; Published: 16 October 2017); https://doi.org/10.1117/12.2282943.
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