• Advanced Photonics Nexus
  • Vol. 4, Issue 1, 016010 (2025)
Jianan Feng1、2, Chang Li1、2, Dahai Yang3、4, Yang Liu1、2, Jianyang Hu1、2, Chen Chen5, Yiqun Wang5, Jie Lin1、6、*, Lei Wang2, and Peng Jin1、2、*
Author Affiliations
  • 1Harbin Institute of Technology, Ministry of Education, Key Laboratory of Micro-systems and Micro-structures Manufacturing, Harbin, China
  • 2Harbin Institute of Technology, School of Instrumentation Science and Engineering, Harbin, China
  • 3Great Bay University, School of Physical Sciences, Dongguan, China
  • 4Great Bay University, Great Bay Institute for Advanced Study, Dongguan, China
  • 5Chinese Academy of Sciences, Suzhou Institute of Nano-Tech and Nano-Bionics, Suzhou, China
  • 6Harbin Institute of Technology, School of Physics, Harbin, China
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    DOI: 10.1117/1.APN.4.1.016010 Cite this Article Set citation alerts
    Jianan Feng, Chang Li, Dahai Yang, Yang Liu, Jianyang Hu, Chen Chen, Yiqun Wang, Jie Lin, Lei Wang, Peng Jin, "Compact planar-waveguide integrated diffractive optical neural network chip," Adv. Photon. Nexus 4, 016010 (2025) Copy Citation Text show less
    References

    [1] A. Krizhevsky, I. Sutskever, G. Hinto. ImageNet classification with deep convolutional neural networks. Commun. ACM, 60, 84-90(2017).

    [2] J. Hirschberg, C. Manning. Advances in natural language processing. Science, 349, 261-266(2015).

    [3] G. Litjens et al. A survey on deep learning in medical image analysis. Med. Image Anal., 42, 60-88(2017).

    [4] I. Kruglov, O. Mishulina, M. Bakirv. Quantile based decision making rule of the neural networks committee for ill-posed approximation problems. Neurocomputing, 96, 74-82(2012).

    [5] M. M. Waldrop. The semiconductor industry will soon abandon its pursuit of Moore’s Law. Now things could get a lot more interesting. Nature, 530, 144-147(2016).

    [6] Q. M. Zhang et al. Artificial neural networks enabled by nanophotonics. Light Sci. Appl., 8, 42(2019).

    [7] G. Wetzstein et al. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2021).

    [8] B. J. Shastri et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021).

    [9] D. Perez et al. Multipurpose silicon photonics signal processor core. Nat. Commun., 8, 636(2017).

    [10] L. Mennel et al. Ultrafast machine vision with 2D material neural network image sensors. Nature, 579, 62-66(2020).

    [11] J. M. Wu et al. Analog optical computing for artificial intelligence. Engineering, 10, 133-145(2022).

    [12] J. Spall, X. X. Guo, A. I. Lvovsky. Hybrid training of optical neural networks. Optica, 9, 803-811(2022).

    [13] T. Yan et al. All-optical graph representation learning using integrated diffractive photonics computing units. Sci. Adv., 8, eabn7630(2022).

    [14] B. Muminov, L. T. Vuong. Fourier optical preprocessing in lieu of deep learning. Optica, 7, 1079-1088(2020).

    [15] Y. C. Shen et al. Deep learning with coherent nanophotonic circuits. Photonics, 11, 441-446(2017).

    [16] T. W. Hughes et al. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica, 5, 864-871(2018).

    [17] J. Feldmann et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 569, 208-214(2019).

    [18] E. Khoram et al. Nanophotonic media for artificial neural inference. Photonics Res., 7, 823-827(2019).

    [19] Z. Wang et al. Integrated photonic metasystem for image classifications at telecommunication wavelength. Nat. Commun., 13, 2131(2022).

    [20] F. Ashtiani, A. J. Geers, F. Aflatouni. An on-chip photonic deep neural network for image classification. Nature, 606, 501-506(2022).

    [21] X. Y. Meng et al. Compact optical convolution processing unit based on multimode interference. Nat. Commun., 14, 3000(2023).

    [22] J. Feldmann et al. Calculating with light using a chip-scale all-optical abacus. Nat. Commun., 8, 1256(2017).

    [23] C. Rios et al. In-memory computing on a photonic platform. Sci. Adv., 5, eaau5759(2019).

    [24] J. Feldmann et al. Parallel convolutional processing using an integrated photonic tensor core. Nature, 589, 52-58(2021).

    [25] H. Zhang et al. An optical neural chip for implementing complex-valued neural network. Nat. Commun., 12, 457(2021).

    [26] H. H. Zhu et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun., 13, 1044(2022).

    [27] T. Z. Fu et al. Photonic machine learning with on-chip diffractive optics. Nat. Commun., 14, 70(2023).

    [28] Y. H. Tang et al. Device-system end-to-end design of photonic neuromorphic processor using reinforcement learning. Laser Photonics Rev., 17, 2200381(2023).

    [29] C. Qian et al. Performing optical logic operations by a diffractive neural network. Light Sci. Appl., 9, 59(2020).

    [30] J. K. Weng et al. Meta-neural-network for real-time and passive deep-learning-based object recognition. Nat. Commun., 11, 6309(2021).

    [31] C. Liu et al. A programmable diffractive deep neural network based on a digital-coding metasurfaces array. Nat. Electron., 5, 113-122(2022).

    [32] X. Y. Xu et al. 11 TOPS photonic convolutional accelerator for optical neural network. Nature, 589, 44-51(2021).

    [33] X. Lin et al. All-optical machine learning using diffractive deep neural network. Science, 361, 1004-1008(2018).

    [34] S. M. Jiao et al. Optical machine learning with incoherent light and a single-pixel detector. Opt. Lett., 44, 5186-5189(2019).

    [35] T. Yan et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett., 123, 023901(2019).

    [36] Y. Luo et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl., 8, 112(2019).

    [37] Y. Zuo et al. All-optical neural network with nonlinear activation functions. Optica, 6, 1132-1137(2019).

    [38] H. K. Dou et al. Residual D2NN: training diffractive deep neural networks via learnable light shortcuts. Opt. Lett., 45, 2688-2691(2020). https://doi.org/10.1364/OL.389696

    [39] T. K. Zhou et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics, 15, 367-373(2021).

    [40] H. Chen et al. Diffractive deep neural networks at visible wavelengths. Engineering, 7, 1483-1491(2021).

    [41] S. S. Rahman et al. Ensemble learning of diffractive optical networks. Light Sci. Appl., 10, 14(2021).

    [42] E. Goi et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl., 10, 40(2021).

    [43] M. Veli et al. Terahertz pulse shaping using diffractive surfaces. Nat. Commun., 12, 37(2021).

    [44] A. Ryou et al. Free-space optical neural network based on thermal atomic nonlinearity. Photonics Res., 9, B128-B134(2021).

    [45] J. X. Li et al. Spectrally encoded single-pixel machine vision using diffractive networks. Sci. Adv., 7, eabd7690(2021).

    [46] J. S. Shi et al. A physics-informed deep learning liquid crystal camera with data-driven diffractive guidance. Commun. Eng., 3, 46(2024).

    [47] Q. Jia et al. Compensating the distorted OAM beams with near zero time delay. Appl. Phys. Lett., 121, 011104(2022).

    [48] Q. Jia et al. Universal translation operator for Laguerre-Gaussian mode sorting. Appl. Phys. Lett., 121, 191104(2022).

    [49] Q. Jia et al. Vector vortex beams sorting of 120 modes in visible spectrum. Nanophotonics, 12, 3955-3962(2023).

    Jianan Feng, Chang Li, Dahai Yang, Yang Liu, Jianyang Hu, Chen Chen, Yiqun Wang, Jie Lin, Lei Wang, Peng Jin, "Compact planar-waveguide integrated diffractive optical neural network chip," Adv. Photon. Nexus 4, 016010 (2025)
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