Hotspot detection using image pattern recognition based on higher-order local auto-correlation

Shimon Maeda; Tetsuaki Matsunawa; Ryuji Ogawa; Hirotaka Ichikawa; Kazuhiro Takahata; Masahiro Miyairi; Toshiya Kotani; Shigeki Nojima; Satoshi Tanaka; Kei Nakagawa; Tamaki Saito; Shoji Mimotogi; Soichi Inoue; Hirokazu Nosato; Hidenori Sakanashi; Takumi Kobayashi; Masahiro Murakawa; Tetsuya Higuchi; Eiichi Takahashi; Nobuyuki Otsu

doi:10.1117/12.881193

4 April 2011 Hotspot detection using image pattern recognition based on higher-order local auto-correlation

Shimon Maeda, Tetsuaki Matsunawa, Ryuji Ogawa, Hirotaka Ichikawa, Kazuhiro Takahata, Masahiro Miyairi, Toshiya Kotani, Shigeki Nojima, Satoshi Tanaka, Kei Nakagawa, Tamaki Saito, Shoji Mimotogi, Soichi Inoue, Hirokazu Nosato, Hidenori Sakanashi, Takumi Kobayashi, Masahiro Murakawa, Tetsuya Higuchi, Eiichi Takahashi, Nobuyuki Otsu

Author Affiliations +

Proceedings Volume 7974, Design for Manufacturability through Design-Process Integration V; 79740X (2011) https://doi.org/10.1117/12.881193
Event: SPIE Advanced Lithography, 2011, San Jose, California, United States

Abstract

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

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Shimon Maeda, Tetsuaki Matsunawa, Ryuji Ogawa, Hirotaka Ichikawa, Kazuhiro Takahata, Masahiro Miyairi, Toshiya Kotani, Shigeki Nojima, Satoshi Tanaka, Kei Nakagawa, Tamaki Saito, Shoji Mimotogi, Soichi Inoue, Hirokazu Nosato, Hidenori Sakanashi, Takumi Kobayashi, Masahiro Murakawa, Tetsuya Higuchi, Eiichi Takahashi, and Nobuyuki Otsu "Hotspot detection using image pattern recognition based on higher-order local auto-correlation", Proc. SPIE 7974, Design for Manufacturability through Design-Process Integration V, 79740X (4 April 2011); https://doi.org/10.1117/12.881193

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