Advanced Photonics Nexus, Volume. 3, Issue 2, 026002(2024)

Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network

Yi Xu1, Fu Li1, Jianqiang Gu1、*, Zhiwei Bi2, Bing Cao2、*, Quanlong Yang3、*, Jiaguang Han4, Qinghua Hu2, and Weili Zhang5
Author Affiliations
  • 1Tianjin University, Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering, Ministry of Education, Key Laboratory of Optoelectronic Information Technology, Tianjin, China
  • 2Tianjin University, College of Intelligence and Computing, Tianjin, China
  • 3Central South University, School of Physics and Electronics, Hunan Key Laboratory of Nanophotonics and Devices, Changsha, China
  • 4Guilin University of Electronic Technology, School of Optoelectronic Engineering, Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin, China
  • 5Oklahoma State University, School of Electrical and Computer Engineering, Stillwater, Oklahoma, United States
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    References(42)

    [38] C. Trabelsi et al. Deep complex networks(2017).

    [39] J. Yosinski et al. How transferable are features in deep neural networks?(2014).

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    Yi Xu, Fu Li, Jianqiang Gu, Zhiwei Bi, Bing Cao, Quanlong Yang, Jiaguang Han, Qinghua Hu, Weili Zhang. Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network[J]. Advanced Photonics Nexus, 2024, 3(2): 026002

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    Paper Information

    Category: Research Articles

    Received: Oct. 9, 2023

    Accepted: Jan. 16, 2024

    Published Online: Apr. 2, 2024

    The Author Email: Gu Jianqiang (gjq@tju.edu.cn), Cao Bing (caobing@tju.edu.cn), Yang Quanlong (quanlong.yang@csu.edu.cn)

    DOI:10.1117/1.APN.3.2.026002

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