Deep learning's impact on pattern matching for design based metrology and design based inspection

Shuyang Dou; Shinichi Shinoda; Masayoshi Ishikawa; Ryo Yumiba; Yasutaka Toyoda; Hiroyuki Shindo; Masayuki Izawa; Masanori Ouchi

doi:10.1117/12.2514921

26 March 2019 Deep learning's impact on pattern matching for design based metrology and design based inspection

Shuyang Dou, Shinichi Shinoda, Masayoshi Ishikawa, Ryo Yumiba, Yasutaka Toyoda, Hiroyuki Shindo, Masayuki Izawa, Masanori Ouchi

Author Affiliations +

Proceedings Volume 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII; 109592G (2019) https://doi.org/10.1117/12.2514921
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

With the miniaturization of devices, hot spot evaluation of a wide area of a wafer for small change points such as wafer topology is required. DBM (Design Based Metrology) is an effective method for evaluating systematic defects of multiple patterning and EUV lithography. However, it takes a long time to evaluate because it is necessary to acquire a high-SN SEM image captured by low-speed SEM scanning conditions. Therefore, we developed a new pattern matching method of DBM by utilizing deep learning technology. Our proposed method can handle low-SN SEM images captured under high-speed SEM scanning conditions.

In the proposed method, we use deep learning to estimate design layout from SEM image, and then perform pattern matching between this estimated design layout and the true design layout. The proposed method is particularly effective for pattern matching of low-SN SEM images and circuit pattern distorted during manufacturing process. It is expected that this method will be advantageous for evaluating mass systematic defects during the process development. Experimental results showed that the proposed method could estimate the design layout from the low-SN SEM image and improve the pattern matching success rate.

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Shuyang Dou, Shinichi Shinoda, Masayoshi Ishikawa, Ryo Yumiba, Yasutaka Toyoda, Hiroyuki Shindo, Masayuki Izawa, and Masanori Ouchi "Deep learning's impact on pattern matching for design based metrology and design based inspection", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 109592G (26 March 2019); https://doi.org/10.1117/12.2514921

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