Machine learning for mask/wafer hotspot detection and mask synthesis

Yibo Lin; Xiaoqing Xu; Jiaojiao Ou; David Z. Pan

doi:10.1117/12.2282943

16 October 2017 Machine learning for mask/wafer hotspot detection and mask synthesis

Yibo Lin, Xiaoqing Xu, Jiaojiao Ou, David Z. Pan

Proceedings Volume 10451, Photomask Technology 2017; 104510A (2017) https://doi.org/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

Citation Download Citation

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 2017, 104510A (16 October 2017); https://doi.org/10.1117/12.2282943

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