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Multi-criteria hotspot detection using pattern classification
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Paper Abstract

Lithography hotspot detection using lithography simulation (LCC) in a design stage is one of important techniques in order to avoid yield loss caused by the hotspots. Conventional LCC should detect all hotspots observed on wafer and reduce false errors which are not hotspots on wafer. However, the conventional LCC is not enough to meet the requirement. In this paper, we propose a multi-criteria hotspot detection method with a pattern classification technique. The proposed method uses a peak intensity value as the criterion and different criteria are used for different pattern categories. The categories are created based on K-means algorithm. Experimental results show our proposed method can reduce a number of false errors by 75% without any overlooking of hotspots.

Paper Details

Date Published: 20 March 2019
PDF: 6 pages
Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620T (20 March 2019); doi: 10.1117/12.2515665
Show Author Affiliations
Kazufumi Shiozawa, Toshiba Memory Corp. (Japan)
Taiki Kimura, Toshiba Memory Corp. (Japan)
Tetsuaki Matsunawa, Toshiba Memory Corp. (Japan)
Shigeki Nojima, Toshiba Memory Corp. (Japan)
Toshiya Kotani, Toshiba Memory Corp. (Japan)


Published in SPIE Proceedings Vol. 10962:
Design-Process-Technology Co-optimization for Manufacturability XIII
Jason P. Cain, Editor(s)

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