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Deep learning's impact on contour extraction for design based metrology and design based inspection
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Paper 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.

Paper Details

Date Published: 26 March 2019
PDF: 9 pages
Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095912 (26 March 2019); doi: 10.1117/12.2514898
Show Author Affiliations
Ryo Yumiba, Hitachi, Ltd. (Japan)
Masayoshi Ishikawa, Hitachi, Ltd. (Japan)
Shinichi Shinoda, Hitachi, Ltd. (Japan)
Shigetoshi Sakimura, Hitachi, Ltd. (Japan)
Yasutaka Toyoda, Hitachi, Ltd. (Japan)
Hiroyuki Shindo, Hitachi High-Technologies Corp. (Japan)
Masayuki Izawa, Hitachi High-Technologies Corp. (Japan)


Published in SPIE Proceedings Vol. 10959:
Metrology, Inspection, and Process Control for Microlithography XXXIII
Vladimir A. Ukraintsev; Ofer Adan, Editor(s)

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