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Impact of feature extraction to accuracy of machine learning based hotspot detection
Author(s): Takashi Mitsuhashi
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Paper Abstract

Machine learning based hot spot detection is an emerging area in verification of mask and layout design. In machine learning, feature extraction methods suitable for application domains are as important as learning and inference algorithm itself for detection accuracy. In this paper, several feature extraction methods were proposed and implemented, and compared using a standard bench mark dataset. Preferable characteristics for the good feature extraction will be discussed. Comparison studies indicated that combination of a good feature extraction method and a standard machine learning algorithm often gave excellent results compared with previously reported results.

Paper Details

Date Published: 16 October 2017
PDF: 10 pages
Proc. SPIE 10451, Photomask Technology, 104510C (16 October 2017); doi: 10.1117/12.2282414
Show Author Affiliations
Takashi Mitsuhashi, Aktina-Solutions LLC (Japan)


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

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