Photonics Research, Volume. 9, Issue 6, B247(2021)

Genetic-algorithm-based deep neural networks for highly efficient photonic device design

Yangming Ren1,2、†, Lingxuan Zhang1,2、†, Weiqiang Wang1,2, Xinyu Wang1,2, Yufang Lei1,2, Yulong Xue1,2, Xiaochen Sun1,2,3、*, and Wenfu Zhang1,2,4、*
Author Affiliations
  • 1State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3e-mail: sunxiaochen@opt.ac.cn
  • 4e-mail: wfuzhang@opt.ac.cn
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    CLP Journals

    [1] Li Gao, Yang Chai, Darko Zibar, Zongfu Yu, "Deep learning in photonics: introduction," Photonics Res. 9, DLP1 (2021)

    [2] Alessia Suprano, Danilo Zia, Emanuele Polino, Taira Giordani, Luca Innocenti, Alessandro Ferraro, Mauro Paternostro, Nicolò Spagnolo, Fabio Sciarrino, "Dynamical learning of a photonics quantum-state engineering process," Adv. Photon. 3, 066002 (2021)

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    Yangming Ren, Lingxuan Zhang, Weiqiang Wang, Xinyu Wang, Yufang Lei, Yulong Xue, Xiaochen Sun, Wenfu Zhang, "Genetic-algorithm-based deep neural networks for highly efficient photonic device design," Photonics Res. 9, B247 (2021)

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    Paper Information

    Special Issue: DEEP LEARNING IN PHOTONICS

    Received: Dec. 1, 2020

    Accepted: Mar. 25, 2021

    Published Online: May. 27, 2021

    The Author Email: Xiaochen Sun (sunxiaochen@opt.ac.cn), Wenfu Zhang (wfuzhang@opt.ac.cn)

    DOI:10.1364/PRJ.416294

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