Sufficient machine learning-based pattern classification for curvilinear layouts

Lianghong Yin; Marko Chew; Shumay Shang; Le Hong; Fan Jiang; Ingo Bork; Ilhami Torunoglu

doi:10.1117/12.3034740

20 November 2024 Sufficient machine learning-based pattern classification for curvilinear layouts

Lianghong Yin, Marko Chew, Shumay Shang, Le Hong, Fan Jiang, Ingo Bork, Ilhami Torunoglu

Proceedings Volume 13216, Photomask Technology 2024; 132162R (2024) https://doi.org/10.1117/12.3034740
Event: SPIE Photomask Technology + EUV Lithography, 2024, Monterey, California, United States

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Abstract

In this paper, we develop a sufficient methodology of machine learning based pattern classification. This sufficient methodology can deal with patterns with mirror symmetry and rotation symmetry. These are needed to classify curvilinear (CL) layouts. This methodology can perform pattern classification on complicated layouts, including both Manhattan and curvilinear shapes. It is especially useful for pattern classification in curvilinear layouts and can be applied to CL Optical Process Correction verification (CL OPCV), CL Mask Process Correction verification (CL MPCV), CL Mask Rule Checking (CL MRC), and beyond.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lianghong Yin, Marko Chew, Shumay Shang, Le Hong, Fan Jiang, Ingo Bork, and Ilhami Torunoglu "Sufficient machine learning-based pattern classification for curvilinear layouts", Proc. SPIE 13216, Photomask Technology 2024, 132162R (20 November 2024); https://doi.org/10.1117/12.3034740

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