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Machine learning for mask/wafer hotspot detection and mask synthesis
Author(s): Yibo Lin; Xiaoqing Xu; Jiaojiao Ou; David Z. Pan
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Paper 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.

Paper Details

Date Published: 16 October 2017
PDF: 13 pages
Proc. SPIE 10451, Photomask Technology, 104510A (16 October 2017); doi: 10.1117/12.2282943
Show Author Affiliations
Yibo Lin, The Univ. of Texas at Austin (United States)
Xiaoqing Xu, The Univ. of Texas at Austin (United States)
Jiaojiao Ou, The Univ. of Texas at Austin (United States)
David Z. Pan, The Univ. of Texas at Austin (United States)


Published in SPIE Proceedings Vol. 10451:
Photomask Technology
Peter D. Buck; Emily E. Gallagher, Editor(s)

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