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Deep Learning-based Automated Measurement Method for Cross-sectional SEM Images in Semiconductor Devices

Published online by Cambridge University Press:  30 July 2020

Yutaka Okuyama  and
Takeshi Ohmori
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. 698 - 700
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Copyright
Copyright © Microscopy Society of America 2020

References

Goodfellow, I. et al. , Deep Learning, MIT Press, 2016.Google Scholar
S. Ren, et al. , “Faster R-CNN: Towards real time object detection with region proposal networks”, IEEE Trans. PAMI, Vol. 39, pp.1137-1149 (2017).10.1109/TPAMI.2016.2577031CrossRefGoogle Scholar
Badrinarayanan, V. et al. , “SegNet: A deep convolutional encoder-decoder architecture for image segmentation”, IEEE Trans. PAMI, Vol. 39, pp.2481-2495 (2017).10.1109/TPAMI.2016.2644615CrossRefGoogle ScholarPubMed