Chemical information extraction from scanning electron microscopy images on the basis of image recognition

Yuqing Jin; Takahiro Kozawa; Kota Aoki; Tomoya Nakamura; Yasushi Makihara; Yasushi Yagi

doi:10.1117/12.2666992

30 April 2023 Chemical information extraction from scanning electron microscopy images on the basis of image recognition

Yuqing Jin, Takahiro Kozawa, Kota Aoki, Tomoya Nakamura, Yasushi Makihara, Yasushi Yagi

Author Affiliations +

Proceedings Volume 12498, Advances in Patterning Materials and Processes XL; 1249825 (2023) https://doi.org/10.1117/12.2666992
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Conference Poster

Abstract

Traditional resist materials have faced challenges as the extreme ultraviolet (EUV) light source with a wavelength of 13.5 nm brought the evolution of lithography to the semiconductor industry. A significant issue in the development of resist materials or the discovery of new type resists is that numerous parameters involved in the resist pattern printing process cause the generation of defects. Meanwhile, the inherent chemical variation in resist materials and processes causes the stochastic defects. In addition, the stochastic defects caused by the inherent chemical variation in resist materials and processes become increasingly significant as feature scales continue to shrink. Consequently, the number of pattern data with failures is much greater than those without defects. However, by utilizing the information contained in pattern failures, chemical parameters can be adjusted to improve resist resolution. In this study, a new method is proposed for evaluating resist patterns with defects by fitting the experimental scanning electronic microscopy (SEM) images of line-and-space patterns with defects to simulated images.

Citation Download Citation

Yuqing Jin, Takahiro Kozawa, Kota Aoki, Tomoya Nakamura, Yasushi Makihara, and Yasushi Yagi "Chemical information extraction from scanning electron microscopy images on the basis of image recognition", Proc. SPIE 12498, Advances in Patterning Materials and Processes XL, 1249825 (30 April 2023); https://doi.org/10.1117/12.2666992

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