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Feature Parameter Design Using Cross-sectional SEM for Machine Learning-based Optimization in Plasma Etching

Published online by Cambridge University Press:  30 July 2020

Takashi Dobashi ,
Hyakka Nakada ,
Yutaka Okuyama  and
Takeshi Ohmori
Takashi Dobashi
Affiliation:
Hitachi High Technologies America, Portland, Oregon, United States
Hyakka Nakada
Affiliation:
Hitachi, Ltd., Research & Development Group, Tokyo, Tokyo, Japan
Yutaka Okuyama
Affiliation:
Hitachi, Ltd., Research & Development Group, Kokubunji, Tokyo, Japan
Takeshi Ohmori
Affiliation:
Hitachi, Ltd., Research & Development Group, Kokubunji, Tokyo, Japan

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 1162 - 1163
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Copyright
Copyright © Microscopy Society of America 2020

References

Ohmori, T., et al. , in Proc. Int. Symp. Dry Process, pp. 9–10, 2017.Google Scholar
Shawe-Taylor, J., et al. , Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.10.1017/CBO9780511809682CrossRefGoogle Scholar