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20 March 2019 Multi-criteria hotspot detection using pattern classification
Kazufumi Shiozawa, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, Toshiya Kotani
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Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620T (2019) https://doi.org/10.1117/12.2515665
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States
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.
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Kazufumi Shiozawa, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, and Toshiya Kotani "Multi-criteria hotspot detection using pattern classification", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620T (20 March 2019); https://doi.org/10.1117/12.2515665
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KEYWORDS
Semiconducting wafers
Critical dimension metrology
Image classification
Photomasks
System on a chip
Convolution
Wafer inspection
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