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26 March 2019 Deep learning's impact on contour extraction for design based metrology and design based inspection
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Abstract
With the miniaturization of devices, hot spots caused by wafer topology are becoming a problem in addition to hot spots resulting from design, mask and wafer process, and hot spot evaluation of a wide area in a chip is becoming required. Although DBM (Design Based Metrology) is an effective method for evaluating systematic defects of EUV lithography and multi-patterning, it requires a long time to evaluate because it is necessary to acquire a high-SN SEM image captured by a contour extraction for DBM that can handle low-SN SEM image captured by high-speed SEM scanning conditions.

Contour extraction using deep learning possesses high noise immunity and excellent pattern recognition ability, and demonstrates high performance to contour extraction from low SN SEM images and multiple layers pattern ones. The proposed method is composed of annotation operation of SEM image samples, training process using annotation data and SEM image samples, and contour extraction process using the trained outcome. In the evaluation experiment, we confirmed that satisfactory contours are extracted from low SN SEM images and multiple layers pattern ones.
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Ryo Yumiba, Masayoshi Ishikawa, Shinichi Shinoda, Shigetoshi Sakimura, Yasutaka Toyoda, Hiroyuki Shindo, and Masayuki Izawa "Deep learning's impact on contour extraction for design based metrology and design based inspection", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095912 (26 March 2019); https://doi.org/10.1117/12.2514898
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