All-Dielectric Metasurface Designs Enabled by Deep Neural Networks

Sensong An; Clayton Fowler; Bowen Zheng; Mikhail Y. Shalaginov; Hong Tang; Hang Li; Jun DIng; Myungkoo Kang; Anuradha Murthy Agarwal; Clara Rivero-Baleine; Kathleen A. Richardson; Tian Gu; Juejun Hu; Hualiang Zhang

All-Dielectric Metasurface Designs Enabled by Deep Neural Networks

Sensong An, Clayton Fowler, Bowen Zheng, Mikhail Y. Shalaginov, Hong Tang, Hang Li, Jun DIng, Myungkoo Kang, Anuradha Murthy Agarwal, Clara Rivero-Baleine, Kathleen A. Richardson, Tian Gu, Juejun Hu, Hualiang Zhang

School of Communication and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We propose a deep learning design approach that significantly improves the design efficiency and accuracy over traditional trial-and-error methods that are currently in use to engineer metasurface-based devices.

Original language	English
Title of host publication	2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781943580767
State	Published - May 2020
Event	2020 Conference on Lasers and Electro-Optics, CLEO 2020 - San Jose, United States Duration: 10 May 2020 → 15 May 2020

Publication series

Name	Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS
Volume	2020-May
ISSN (Print)	1092-8081

Conference

Conference	2020 Conference on Lasers and Electro-Optics, CLEO 2020
Country/Territory	United States
City	San Jose
Period	10/05/20 → 15/05/20

Cite this

An, S., Fowler, C., Zheng, B., Shalaginov, M. Y., Tang, H., Li, H., DIng, J., Kang, M., Agarwal, A. M., Rivero-Baleine, C., Richardson, K. A., Gu, T., Hu, J., & Zhang, H. (2020). All-Dielectric Metasurface Designs Enabled by Deep Neural Networks. In 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings Article 9193264 (Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc..

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title = "All-Dielectric Metasurface Designs Enabled by Deep Neural Networks",

abstract = "We propose a deep learning design approach that significantly improves the design efficiency and accuracy over traditional trial-and-error methods that are currently in use to engineer metasurface-based devices.",

author = "Sensong An and Clayton Fowler and Bowen Zheng and Shalaginov, \{Mikhail Y.\} and Hong Tang and Hang Li and Jun DIng and Myungkoo Kang and Agarwal, \{Anuradha Murthy\} and Clara Rivero-Baleine and Richardson, \{Kathleen A.\} and Tian Gu and Juejun Hu and Hualiang Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 OSA.; 2020 Conference on Lasers and Electro-Optics, CLEO 2020 ; Conference date: 10-05-2020 Through 15-05-2020",

year = "2020",

month = may,

language = "英语",

series = "Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings",

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An, S, Fowler, C, Zheng, B, Shalaginov, MY, Tang, H, Li, H, DIng, J, Kang, M, Agarwal, AM, Rivero-Baleine, C, Richardson, KA, Gu, T, Hu, J & Zhang, H 2020, All-Dielectric Metasurface Designs Enabled by Deep Neural Networks. in 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings., 9193264, Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS, vol. 2020-May, Institute of Electrical and Electronics Engineers Inc., 2020 Conference on Lasers and Electro-Optics, CLEO 2020, San Jose, United States, 10/05/20.

All-Dielectric Metasurface Designs Enabled by Deep Neural Networks. / An, Sensong; Fowler, Clayton; Zheng, Bowen et al.
2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9193264 (Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS; Vol. 2020-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - All-Dielectric Metasurface Designs Enabled by Deep Neural Networks

AU - An, Sensong

AU - Fowler, Clayton

AU - Zheng, Bowen

AU - Shalaginov, Mikhail Y.

AU - Tang, Hong

AU - Li, Hang

AU - DIng, Jun

AU - Kang, Myungkoo

AU - Agarwal, Anuradha Murthy

AU - Rivero-Baleine, Clara

AU - Richardson, Kathleen A.

AU - Gu, Tian

AU - Hu, Juejun

AU - Zhang, Hualiang

PY - 2020/5

Y1 - 2020/5

N2 - We propose a deep learning design approach that significantly improves the design efficiency and accuracy over traditional trial-and-error methods that are currently in use to engineer metasurface-based devices.

AB - We propose a deep learning design approach that significantly improves the design efficiency and accuracy over traditional trial-and-error methods that are currently in use to engineer metasurface-based devices.

UR - https://www.scopus.com/pages/publications/85091648930

M3 - 会议稿件

AN - SCOPUS:85091648930

T3 - Conference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS

BT - 2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Conference on Lasers and Electro-Optics, CLEO 2020

Y2 - 10 May 2020 through 15 May 2020

ER -

All-Dielectric Metasurface Designs Enabled by Deep Neural Networks

Abstract

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Conference

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