留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

插损鲁棒性的全复值光学神经网络

陈慧彬 汤凯飞 游振宇

陈慧彬, 汤凯飞, 游振宇. 插损鲁棒性的全复值光学神经网络[J]. 中国光学(中英文). doi: 10.37188/CO.2023-0198
引用本文: 陈慧彬, 汤凯飞, 游振宇. 插损鲁棒性的全复值光学神经网络[J]. 中国光学(中英文). doi: 10.37188/CO.2023-0198
CHEN Hui-bin, TANG Kai-fei, YOU Zhen-yu. Fully complex optical neural network with insertion-loss robustness[J]. Chinese Optics. doi: 10.37188/CO.2023-0198
Citation: CHEN Hui-bin, TANG Kai-fei, YOU Zhen-yu. Fully complex optical neural network with insertion-loss robustness[J]. Chinese Optics. doi: 10.37188/CO.2023-0198

插损鲁棒性的全复值光学神经网络

doi: 10.37188/CO.2023-0198
基金项目: 国家自然科学基金(No. 61705119)
详细信息
    作者简介:

    陈慧彬(1985—),女,福建宁德人,2016年于中国科学院大学获得凝聚态物理专业理学博士学位,现任泉州师范学院副教授,硕士生导师。主要从事固体激光器、半导体激光器、光神经网络的研究。E-mail:chenhuibin@qztc.edu.cn

    游振宇(1980—),男,福建福州人,2020年于中国科学院大学获得凝聚态物理专业理学博士学位,现任泉州师范学院副教授。主要从事全固态激光晶体及激光技术的研究。E-mail:youshower@qztc.edu.cn

  • 中图分类号: TP394.1;TH691.9

Fully complex optical neural network with insertion-loss robustness

Funds: Supported by the National Natural Science Foundation of China (No. 61705119)
More Information
  • 摘要:

    基于马赫-曾德尔干涉仪(MZI: Mach-Zehnder Interferometer)级联拓扑结构的线性光学处理器被证明是实现光学神经网络(ONN: optical neural network)的重要途径,但还有不少实际问题有待解决。针对芯片制造、测试过程中可能导致的相位误差和插入损耗等问题,我们通过实验和理论仿真了几种基于MZI结构的可重构光学处理器。发现可以通过单个N×N的Clements阵列结构来实现任意酉矩阵的权重,构建稀疏连接的全复值光学神经网络,将光学深度大大降低,以实现较高的插入损耗鲁棒性。此外,对于多层光学神经网络来说,由于构建该任意酉矩阵的自由度有限,我们设计在每一层Clements结构前面加一个相移器层,有助于将分类数据映射到更高的数据维度,能实现神经网络更快速的收敛。

     

  • 图 1  单个2×2 的MZI器件

    Figure 1.  Single 2×2 MZI device

    图 2  三种典型的MZI阵列拓扑结构

    Figure 2.  Three typical MZI array topologies

    图 3  两种MZI阵列拓扑结构的插损与相位敏感性

    Figure 3.  Insertion-loss and phase sensitivity of two types of MZI array topologies

    图 4  一种快速收敛的拓扑架构和对应的神经网络示意图

    Figure 4.  Diagram of rapidly converging topology and corresponding neural network

    图 5  多维聚类高斯分布数据的分类任务

    Figure 5.  Classification tasks for multidimensional clustering Gaussian-distribution data

    图 6  两种双层光学神经网络芯片的训练过程

    Figure 6.  The training process of two double-layer optical neural network chips

    图 7  Iris数据在双层光学神经网络中的训练及分类结果

    Figure 7.  Training and classification results of Iris data in double-layer optical neural networks

    表  1  第一层Clements结构中相移器的相位值

    Table  1.   The value of the phase shifter in the first layer Clements structure

    MZI (1) (2) (3) (4) (5) (6)
    θ(rad) 1.354 2.518 1.683 2.614 2.614 6.248
    φ(rad) 1.064 4.881 0.995 2.175 1.535 0.130
    下载: 导出CSV

    表  2  第二层Clements结构中相移器的相位值

    Table  2.   The value of the phase shifter in the second layer Clements structure

    MZI (1) (2) (3) (4) (5) (6)
    θ(rad) 0.393 1.452 0.270 0.505 5.662 1.250
    φ(rad) 5.447 3.434 2.740 0.700 5.416 5.690
    下载: 导出CSV
  • [1] 周治平, 许鹏飞, 董晓文. 硅基光电计算[J]. 中国激光,2020,47(6):0600001. doi: 10.3788/CJL202047.0600001

    ZHOU ZH P, XU P F, DONG X W. Computing on silicon photonic platform[J]. Chinese Journal of Lasers, 2020, 47(6): 0600001. (in Chinese). doi: 10.3788/CJL202047.0600001
    [2] 王之江. 光计算技术进展[J]. 中国科学院院刊,1987,2(3):198-205.

    WANG ZH J. Progress in optical computing technology[J]. Bulletin of Chinese Academy of Sciences,1987,2(3):198-205. (in Chinese) .
    [3] 周宏强, 黄玲玲, 王涌天. 深度学习算法及其在光学的应用[J]. 红外与激光工程,2019,48(12):1226004. doi: 10.3788/IRLA201948.1226004

    ZHOU H Q, HUANG L L, WANG Y T. Deep learning algorithm and its application in optics[J]. Infrared and Laser Engineering, 2019, 48(12): 1226004. (in Chinese). doi: 10.3788/IRLA201948.1226004
    [4] FELDMANN J, YOUNGBLOOD N, WRIGHT C D, et al. All-optical spiking neurosynaptic networks with self-learning capabilities[J]. Nature, 2019, 569(7755): 208-214. doi: 10.1038/s41586-019-1157-8
    [5] RÍOS C, YOUNGBLOOD N, CHENG Z G, et al. In-memory computing on a photonic platform[J]. Science Advances, 2019, 5(2): eaau5759. doi: 10.1126/sciadv.aau5759
    [6] SHASTRI B J, TAIT A N, FERREIRA DE LIMA T, et al. Photonics for artificial intelligence and neuromorphic computing[J]. Nature Photonics, 2021, 15(2): 102-114. doi: 10.1038/s41566-020-00754-y
    [7] 谢意维, 张涛, 戴道锌. 智能化可重构硅光集成器件及芯片应用研究[J]. 中兴通讯技术,2020,26(2):64-69. doi: 10.12142/ZTETJ.202002009

    XIE Y W, ZHANG T, DAI D X. Applications of intelligent reconfigurable silicon photonic devices and circuits[J]. ZTE Technology Journal, 2020, 26(2): 64-69. (in Chinese). doi: 10.12142/ZTETJ.202002009
    [8] 王俊, 杨晓飞. 光子芯片研究进展及展望[J]. 世界科学,2020(12):29-31.

    WANG J, YANG X F. Research progress and prospects of photonic chips[J]. World Science, 2020(12): 29-31. .
    [9] CLEMENTS W R, HUMPHREYS P C, METCALF B J, et al. Optimal design for universal multiport interferometers[J]. Optica, 2016, 3(12): 1460-1465. doi: 10.1364/OPTICA.3.001460
    [10] ZOU W W, MA B W, XU SH F, et al. Towards an intelligent photonic system[J]. Science China Information Sciences, 2020, 63(6): 160401. doi: 10.1007/s11432-020-2863-y
    [11] MARQUEZ B A, HUANG CH R, PRUCNAL P R, et al. Neuromorphic silicon photonics for artificial intelligence [M]//LOCKWOOD D J, PAVESI L. Silicon Photonics IV. Cham: Springer, 2021: 417-447.
    [12] 郑一臻, 戴键, 张天, 等. 基于异构光子神经网络的多模态特征融合[J]. 中国光学(中英文),2023,16(6):1343-1355. doi: 10.37188/CO.2023-0036

    ZHENG Y Z, DAI W, ZHANG T, et al. Multimodal feature fusion based on heterogeneous optical neural networks[J]. Chinese Optics, 2023, 16(6): 1343-1355. (in Chinese). doi: 10.37188/CO.2023-0036
    [13] GIAMOUGIANNIS G, TSAKYRIDIS A, MORALIS-PEGIOS M, et al. Universal linear optics revisited: new perspectives for neuromorphic computing with silicon photonics[J]. IEEE Journal of Selected Topics in Quantum Electronics, 2023, 29(2): 6200116.
    [14] BANDYOPADHYAY S, HAMERLY R, ENGLUND D. Hardware error correction for programmable photonics[J]. Optica, 2021, 8(10): 1247-1255. doi: 10.1364/OPTICA.424052
    [15] WILLIAMSON I A D, HUGHES T W, MINKOV M, et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks[J]. IEEE Journal of Selected Topics in Quantum Electronics, 2020, 26(1): 7700412.
    [16] SPALL J, GUO X X, BARRETT T D, et al. Fully reconfigurable coherent optical vector–matrix multiplication[J]. Optics Letters, 2020, 45(20): 5752-5755. doi: 10.1364/OL.401675
    [17] 谢锋, 朱硕隆, 张振荣. 分光比可调的光功率分束器的设计[J]. 中国光学(中英文),2023,16(5):1121-1128. doi: 10.37188/CO.2023-0038

    XIE F, ZHU SH L, ZHANG ZH R. Design of an optical power splitter with adjustable split ratio[J]. Chinese Optics, 2023, 16(5): 1121-1128. (in Chinese). doi: 10.37188/CO.2023-0038
    [18] ZHANG H, GU M, JIANG X D, et al. An optical neural chip for implementing complex-valued neural network[J]. Nature Communications, 2021, 12(1): 457. doi: 10.1038/s41467-020-20719-7
    [19] SHOKRANEH. F, NEZAMI M S, LIBOIRON-LADOUCEUR O. Theoretical and experimental analysis of a 44 recongurable MZI-based linear optical processor[J]. Journal of Lightwave Technology, 2021: 1. .
    [20] SHOKRANEH F, GEOFFROY-GAGNON S, LIBOIRON-LADOUCEUR O. The diamond mesh, a phase-error- and loss-tolerant field-programmable MZI-based optical processor for optical neural networks[J]. Optics Express, 2020, 28(16): 23495-23508. doi: 10.1364/OE.395441
    [21] TSAKYRIDIS A, GIAMOUGIANNIS G, TOTOVIC A, et al. Fidelity restorable universal linear optics[J]. Advanced Photonics Research, 2022, 3(10): 2200001. doi: 10.1002/adpr.202200001
    [22] SHI Y, REN J Y, CHEN G Y, et al. Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks[J]. Nature Communications, 2022, 13(1): 6048. doi: 10.1038/s41467-022-33877-7
    [23] SHAO R, ZHANG G, GONG X. Generalized robust training scheme using genetic algorithm for optical neural networks with imprecise components[J]. Photonics Research, 2022, 10(8): 1868-1876. doi: 10.1364/PRJ.449570
    [24] HAMERLY R, BANDYOPADHYAY S, ENGLUND D. Asymptotically fault-tolerant programmable photonics[J]. Nature Communications, 2022, 13(1): 6831. doi: 10.1038/s41467-022-34308-3
    [25] RECK M, ZEILINGER A, BERNSTEIN H J, et al. Experimental realization of any discrete unitary operator[J]. Physical Review Letters, 1994, 73(1): 58-61. doi: 10.1103/PhysRevLett.73.58
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  49
  • HTML全文浏览量:  24
  • PDF下载量:  9
  • 被引次数: 0
出版历程
  • 网络出版日期:  2024-05-15

目录

    /

    返回文章
    返回

    重要通知

    2024年2月16日科睿唯安通过Blog宣布,2024年将要发布的JCR2023中,229个自然科学和社会科学学科将SCI/SSCI和ESCI期刊一起进行排名!《中国光学(中英文)》作为ESCI期刊将与全球SCI期刊共同排名!