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Chinese Journal of Lasers, Volume. 51, Issue 1, 0119002(2024)

Review of On‑Chip Integrated Optical Neural Networks (Invited)

Tingzhao Fu1,4,5, Run Sun2,3, Yuyao Huang2,3, Jianfa Zhang1,4,5, Sigang Yang2,3, Zhihong Zhu1,4,5, and Hongwei Chen2,3、*
Author Affiliations
  • 1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, Hunan, China
  • 2Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
  • 3Beijing National Research Center for Information Science and Technology, Beijing 100084, China
  • 4Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices, National University of Defense Technology, Changsha 410073, Hunan, China
  • 5Nanhu Laser Laboratory, National University of Defense Technology, Changsha 410073, Hunan, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
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    References (86)
    Cited By (6)
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    Figures & Tables(9)
    On-chip optical neural networks based on MZI interference structure. (a) MZI topology cascaded array[25]; (b) ONN supporting in-situ online training and gradient backpropagation[27]

    Fig. 1. On-chip optical neural networks based on MZI interference structure. (a) MZI topology cascaded array[25]; (b) ONN supporting in-situ online training and gradient backpropagation[27]

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    Improved on-chip optical neural networks based on MZI interference structure. (a) Complex ONN based on MZI array[41]; (b) ONN based on MZI array and diffractive units[47]

    Fig. 2. Improved on-chip optical neural networks based on MZI interference structure. (a) Complex ONN based on MZI array[41]; (b) ONN based on MZI array and diffractive units[47]

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    On-chip optical neural networks based on MRR wavelength division structure. (a) ONN based on MRR wavelength division system and PCM units[31]; (b) ONN for fiber nonlinear compensation[43]

    Fig. 3. On-chip optical neural networks based on MRR wavelength division structure. (a) ONN based on MRR wavelength division system and PCM units[31]; (b) ONN for fiber nonlinear compensation[43]

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    On-chip optical neural networks based on MRRs and other auxiliary optical devices. (a) ONN based on MRR cross array and supporting gradient backpropagation[57]; (b) ONN system integrating light sources, data loading areas, and data processing units on single chip[59]

    Fig. 4. On-chip optical neural networks based on MRRs and other auxiliary optical devices. (a) ONN based on MRR cross array and supporting gradient backpropagation[57]; (b) ONN system integrating light sources, data loading areas, and data processing units on single chip[59]

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    On-chip diffractive optical neural networks verified by simulation. (a) DONN with computational unit characterized by single subwavelength diffractive structure [35]; (b) DONN with computational unit characterized by subwavelength diffractive structure group[44]

    Fig. 5. On-chip diffractive optical neural networks verified by simulation. (a) DONN with computational unit characterized by single subwavelength diffractive structure [35]; (b) DONN with computational unit characterized by subwavelength diffractive structure group[44]

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    On-chip diffractive optical neural networks verified by experiment. (a) DONN with computational unit composed by two identical subwavelength diffractive structures[50]; (b) DONN with computational unit composed by three identical subwavelength diffractive structures [60]

    Fig. 6. On-chip diffractive optical neural networks verified by experiment. (a) DONN with computational unit composed by two identical subwavelength diffractive structures[50]; (b) DONN with computational unit composed by three identical subwavelength diffractive structures [60]

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    On-chip optical neural networks based on other structures. (a) ONN based on 3D integrated waveguide array[85]; (b) designed ONN based on inverse design method[36]

    Fig. 7. On-chip optical neural networks based on other structures. (a) ONN based on 3D integrated waveguide array[85]; (b) designed ONN based on inverse design method[36]

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    ONN based on waveguide attenuation modulators[53]

    Fig. 8. ONN based on waveguide attenuation modulators[53]

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    • Table 1. Partial performance comparison of on-chip integrated ONNs

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      Table 1. Partial performance comparison of on-chip integrated ONNs

      ReferenceBasic unitTheoretical integration /（NBU /mm2）Operational power consumption /（J/operation）Throughput /TOPS
      Ref.［25］MZI<107.66×10-146.4
      Ref.［41］MZI<102.14×10-1321.6
      Ref.［42］MRR covered with PCM<55.9×10-1528.8
      Ref.［47］MZI and Diffractive cell<201.41×10-1532
      Ref.［50］Subwavelength unit~6.7×1034.2×10-194.05×104
      Ref.［60］Subwavelength unit~2×1031.1×10-171.38×104
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    Tingzhao Fu, Run Sun, Yuyao Huang, Jianfa Zhang, Sigang Yang, Zhihong Zhu, Hongwei Chen. Review of On‑Chip Integrated Optical Neural Networks (Invited)[J]. Chinese Journal of Lasers, 2024, 51(1): 0119002

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    Paper Information

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    Received: Sep. 21, 2023

    Accepted: Oct. 24, 2023

    Published Online: Jan. 19, 2024

    The Author Email: Chen Hongwei (chenhw@tsinghua.edu.cn)

    DOI:10.3788/CJL231227

    CSTR:32183.14.CJL231227

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology