搜索

x

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

短内腔激光器对光子储备池计算的优化

赵彤 谢文丽 许俊伟 贾志伟

引用本文:
Citation:

短内腔激光器对光子储备池计算的优化

赵彤, 谢文丽, 许俊伟, 贾志伟

Optimization of photonic reservoir computing using short internal cavity laser

Zhao Tong, Xie Wen-Li, Xu Jun-Wei, Jia Zhi-Wei
PDF
HTML
导出引用
  • 随着高速信息时代的来临及信息量的爆炸式增长, 对信息处理速度提出了更高的要求, 光子储备池计算系统成为了解决方案之一. 短光子寿命易于提升光子储备池计算系统的响应速度而有助于实现更高速率的信息处理. 激光器内腔长度会影响光子寿命, 同时还影响了激光器输出进入不同动力学状态时所需的相关参数值. 因此, 本文研究了不同内腔长度(120—900 μm)对基于分布式反馈激光器的储备池计算系统性能及相关参数空间的影响. 结果表明, 当内腔长度在120—171 μm范围内, 系统可低误差处理20 Gbps速率的信息; 内腔长度介于120—380 μm之间时、较大的频率失谐及少量虚拟节点数(50), 仍可使系统具有良好的预测效果; 内腔长度较短时, 反馈强度与注入强度组成的高性能参数空间可提高22%—40%.
    With the advent of the high-speed information age and the explosive growth of the information, higher requirements have been placed on the information processing speed. In recent years, the delay-based reservoir computing (RC) systems have been extensively investigated. Meanwhile, the information processing rate is improved mainly around the replacement of nonlinear nodes in the system. Nevertheless, as the most commonly used distributed feedback semiconductor (DFB) laser, many researchers only use ordinary commercial DFB products for research, and they have not noticed the improvement of RC performance caused by changes in internal parameters of laser. With the development of photonic integration technology, the processing technology of DFB turns more mature, so that the size of DFB can be fabricated in a range of 100 μm–1 mm when it still generates laser, and the photon lifetime of the laser will also change. The shorter photon lifetime in the laser leads to a faster dynamic response, which has the potential to process the information at a higher rate in the RC system. According to the laser rate equation (Lang-Kobayashi), changing the internal cavity length will affect the feedback strength, injection strength and other parameters required for the laser to enter into each dynamic state, which in turn affects the parameter space required for the RC system to exhibit high performance. According to this, we study the relationship between the internal cavity length (120 μm–900 μm) and the information processing rate of the RC system. In addition, the influences of different internal cavity lengths on the parameter space of the RC system are analyzed. The results show that when the internal cavity length is in a range from 120 μm to 171 μm, the system can achieve 20-Gbps low-error information processing. It is worth noting that when the internal cavity length decreases from 600 μm to 128 μm, the parameter space with better prediction performance of the RC system is greatly improved. When performing the Santa-Fe chaotic time series prediction task, the normalized mean square error (NMSE) is less than 0.01, and the parameter range of the injection strength is increased by about 22%. The range of parameter with NMSE no more than 0.1 is improved by nearly 40% for the 10th order nonlinear auto-regressive moving average (NARMA-10) task. When the number of virtual nodes is 50, the system can achieve a high-precision prediction for the above two tasks. This is of great significance for the practical development of the system.
      通信作者: 贾志伟, jiazhiwei@tyut.edu.cn
    • 基金项目: 国家重点研发计划(批准号: 2019YFB1803500)、国家自然科学基金(批准号: 61705160, 61961136002, 61875147, 62075154)、山西省自然科学基金(批准号: 20210302123183)和山西省“1331工程”重点创新团队建设计划.
      Corresponding author: Jia Zhi-Wei, jiazhiwei@tyut.edu.cn
    • Funds: Project supported by the National Key R&D Program of China (Grant No. 2019YFB1803500), the National Natural Science Foundation of China (Grant Nos. 61705160, 61961136002, 61875147, 62075154), the Natural Science Foundation of Shanxi Province, China (Grant No. 20210302123183), and the Shanxi “1331 Project” Key Innovative Research Team.
    [1]

    Lukosevicius M, Jaeger H 2009 Comput. Sci. Rev. 3 127Google Scholar

    [2]

    Maass W, Natschläger T, Markram H 2002 Neural Comput 14 2531Google Scholar

    [3]

    Jaeger H, Haas H 2004 Science 304 78Google Scholar

    [4]

    Sunada S, Uchida A 2021 Optica 8 1388Google Scholar

    [5]

    Nakajima M, Tanaka K, Hashimoto T 2021 Commun. Phys. 4 20Google Scholar

    [6]

    Pearlmutter B A 1995 IEEE Trans. Neural Networks 6 1212Google Scholar

    [7]

    Bishop C M 2006 (New York: Springer-Verlag)

    [8]

    Appeltant L, Soriano M C, Van D S G, Danckaert J, Massar S, Dambre J, Schrauwen B, Mirasso C R, Fisher I 2011 Nat. Commun. 2 468Google Scholar

    [9]

    Haynes N D, Soriano M C, Rosin D P, Fischer I, Gauthier D J 2015 Phys. Rev. E 91 020801Google Scholar

    [10]

    Paquot Y, Duport F, Smerieri A, Dambre J, Schrauwen B, Haelterman M, Massar S 2012 Sci. Rep. 2 287Google Scholar

    [11]

    Du W, Li C H, Huang Y X, Zou J H, Luo L Z, Teng C H, Kuo H C, Wu J, Wang Z M 2022 IEEE Electron Device Lett. 43 406Google Scholar

    [12]

    Kanno K, Uchida A 2022 Sci. Rep. 12 3720Google Scholar

    [13]

    Soriano M C, Ortín S, Brunner D, Larger L, Mirasso C R, Fischer I, Pesquera L 2013 Opt. Express 21 12Google Scholar

    [14]

    Duport F, Schneider B, Smerieri A, Haelterman M, Massar S 2012 Opt. Express 20 22783Google Scholar

    [15]

    Genty G, Salmela L, Dudley J M, Brunner D, Kokhanovskiy, Kobtsev S, Turitsyn S K 2020 Nat. Photonics 15 91Google Scholar

    [16]

    花飞, 方捻, 王陆唐 2019 物理学报 68 224205Google Scholar

    Hua F, Fang N, Wang L T 2019 Acta Phys. Sin. 68 224205Google Scholar

    [17]

    刘奇, 李璞, 开超, 胡 春强, 蔡强, 张建国, 徐兵杰 2021 物理学报 70 154209Google Scholar

    Liu Q, Li P, Kai C, Hu C Q, Cai Q, Zhang J G, Xu B J 2021 Acta Phys. Sin. 70 154209Google Scholar

    [18]

    Argyris A, Schwind J, Fischer I 2021 Sci. Rep 11 6701Google Scholar

    [19]

    Tanaka G, Yamane T, Héroux J B, Nakane R, Kanazawa N, Takeda S, Numata H, Nakano D, Hirose A 2019 Neural Networks 115 100Google Scholar

    [20]

    Lugnan A, Katumba A, Laporte F, Freiberger M, Bienstman P 2020 APL Photonics 5 020901Google Scholar

    [21]

    Cai Q, Guo Y, Li P, Bogris A, Wang Y 2021 Photonics Res 9 14Google Scholar

    [22]

    Brunner D, Soriano M C, Mirasso C R, Fischer I 2013 Nat. Commun 4 1364Google Scholar

    [23]

    Nguimdo R M, Verschaffelt G, Danckaert J, Guy V D S 2015 IEEE Trans. Neural Networks Learn. Syst 26 3301Google Scholar

    [24]

    Vatin J, Rontani D, Sciamanna M 2018 Opt. Lett 43 4497Google Scholar

    [25]

    Nguimdo R M, Erneux T 2019 Opt. Lett 44 49Google Scholar

    [26]

    Bogris A, Mesaritakis C, Deligiannidis S, Li P 2020 IEEE J. Sel. Top. Quantum Electron 27 1Google Scholar

    [27]

    Guo X X, Xiang S Y, Zhang Y H, Lin L, Wen A J, Hao Y 2020 IEEE J. Sel. Top. Quantum Electron 26 1Google Scholar

    [28]

    Huang Y, Zhou P, Yang Y, Li N Q 2021 Opt. Lett 46 6035Google Scholar

    [29]

    Kuriki Y, Nakayama J, Takano K, Uchia A 2018 Opt. Express 26 5777Google Scholar

    [30]

    Wang D, Wang L, Zhao T, Gao H, Wang Y, Chen X, Wang A 2017 Opt. Express 25 10911Google Scholar

    [31]

    Argyris A, Bueno J, Fischer I 2018 Sci. Rep 8 8487Google Scholar

    [32]

    Hou Y S, Xia G Q, Yang W Y, Wang D, Jayaprasath E, Jiang Z F, Hu C X, Wu Z M 2018 Opt. Express 26 10211Google Scholar

    [33]

    Lang R, Kobayashi K 1980 IEEE J. Quantum Electron 16 347Google Scholar

    [34]

    Ohtsubo J 2017 Springer Series in Optical Sciences (USA)

    [35]

    Berre M L, Ressayre E, Talleta A, Gibbs H M, Kaplan D L, Rose M H 1987 Phys. Rev. A 35 4020Google Scholar

    [36]

    Jaeger H 2002 Conference and Workshop on Neural Information Processing Systems Vancouver Canada, Nips December 9–14, 2002 p609

    [37]

    Yue D Z, Wu Z M, Hou Y S, Hu C X, Xia G Q 2021 IEEE Photonics J 13 1Google Scholar

    [38]

    Estébanez I, Schwind J, Fischer I, Argyris A 2020 Nanophotonics 9 4163Google Scholar

  • 图 1  基于半导体激光器的延时型储备池计算系统示意图

    Fig. 1.  Schematic diagram of a time-delayed reservoir computing system based on semiconductor laser.

    图 2  DFB激光器随着反馈强度变化的分岔图 (a) l = 128 μm; (b) l = 300 μm; (c) l = 600 μm. I = 1.05Ith, τ = 0.2 ns

    Fig. 2.  The bifurcation diagram of the DFB laser as a function of feedback strength κf: (a) l = 128 μm; (b) l = 300 μm; (c) l = 600 μm. I = 1.05Ith, τ = 0.2 ns.

    图 3  基于Santa-Fe混沌时间序列预测任务的 (a) 不同信息处理速率下内腔长度对处理效果的影响, (b) 内腔长度与信息处理速率参数空间中NMSEs的二维图. I = 1.05Ith, M = 50, κinj = 0.5, κf = 0.5%, Δv = 20 GHz

    Fig. 3.  Based on Santa-Fe chaotic time series prediction task: (a) The influence of internal cavity length on processing effect under different information processing rate; (b) two dimensional maps of NMSEs in parameter space of internal cavity length and information processing rate. I = 1.05Ith, M = 50, κinj = 0.5, κf = 0.5%, Δv = 20 GHz.

    图 4  基于NARMA-10任务的 (a) 不同信息处理速率下内腔长度对处理效果的影响, (b) 在内腔长度与信息处理速率参数空间中NMSEs的二维图. I = 1.05Ith, M = 50, κinj = 0.5, κf = 0.5%, Δv = 20 GHz

    Fig. 4.  Based on NARMA-10 task: (a) The influence of internal cavity length on processing effect under different information processing rate; (b) two dimensional maps of NMSEs in parameter space of internal cavity length and information processing rate. I = 1.05Ith, M = 50, κinj = 0.5, κf = 0.5%, Δv = 20 GHz.

    图 5  不同虚拟节点数下内腔长度对NMSE的影响 (a) Santa-Fe混沌时间序列预测任务; (b) NARMA-10任务. I = 1.05 Ith, M = 50, κinj = 0.5, κf = 1%, Δv = 20 GHz

    Fig. 5.  The influence of internal cavity length on NMSE under different numbers of virtual nodes: (a) Santa-Fe chaotic time series prediction task; (b) NARMA-10 task. I = 1.05 Ith, M = 50, κinj = 0.5, κf = 1%, Δv = 20 GHz.

    图 6  在内腔长度与频率失谐参数空间中NMSEs的二维图 (a) Santa-Fe混沌时间序列预测任务; (b) NARMA-10任务. I = 1.05 Ith, M = 50, κinj = 0.5, κf = 0.5%

    Fig. 6.  Two dimensional maps of NMSEs in parameter space of internal cavity length and frequency detuning: (a) Santa-Fe chaotic time series prediction task; (b) NARMA-10 task. I = 1.05 Ith, M = 50, κinj = 0.5, κf = 0.5%.

    图 7  不同内腔长度下注入强度和反馈强度对NMSE的影响 (a), (b) l = 128 μm; (c), (d) l = 300 μm; (e), (f) l = 600 μm. 从上到下: Santa-Fe混沌时间序列预测任务、NARMA-10任务. I = 1.05 Ith, M = 50, Δv = 20 GHz

    Fig. 7.  The influence of injection strength and feedback strength on NMSE under different internal cavity length: (a), (b) l = 128 μm; (c), (d) l = 300 μm; (e), (f) l = 600 μm. From top to bottom: Santa-Fe chaotic time series prediction task, NARMA-10 task. I = 1.05 Ith, M = 50, Δv = 20 GHz.

    表 1  数值模拟中DFB的部分参数

    Table 1.  Partial parameters of DFB in numerical simulation.

    符号参数参考值
    α线宽增强因子4
    g/ns–1增益系数1.2 × 10–5
    N0透明载流子数1.5 × 108
    ε/m3增益饱和系数5 × 10–23
    τs/ns载流子寿命2
    λ0/nm波长1550
    D/ns–1自发辐射噪声强度30
    Einj, 0注入电场平均幅度100
    bbias偏置项0.5
    下载: 导出CSV
  • [1]

    Lukosevicius M, Jaeger H 2009 Comput. Sci. Rev. 3 127Google Scholar

    [2]

    Maass W, Natschläger T, Markram H 2002 Neural Comput 14 2531Google Scholar

    [3]

    Jaeger H, Haas H 2004 Science 304 78Google Scholar

    [4]

    Sunada S, Uchida A 2021 Optica 8 1388Google Scholar

    [5]

    Nakajima M, Tanaka K, Hashimoto T 2021 Commun. Phys. 4 20Google Scholar

    [6]

    Pearlmutter B A 1995 IEEE Trans. Neural Networks 6 1212Google Scholar

    [7]

    Bishop C M 2006 (New York: Springer-Verlag)

    [8]

    Appeltant L, Soriano M C, Van D S G, Danckaert J, Massar S, Dambre J, Schrauwen B, Mirasso C R, Fisher I 2011 Nat. Commun. 2 468Google Scholar

    [9]

    Haynes N D, Soriano M C, Rosin D P, Fischer I, Gauthier D J 2015 Phys. Rev. E 91 020801Google Scholar

    [10]

    Paquot Y, Duport F, Smerieri A, Dambre J, Schrauwen B, Haelterman M, Massar S 2012 Sci. Rep. 2 287Google Scholar

    [11]

    Du W, Li C H, Huang Y X, Zou J H, Luo L Z, Teng C H, Kuo H C, Wu J, Wang Z M 2022 IEEE Electron Device Lett. 43 406Google Scholar

    [12]

    Kanno K, Uchida A 2022 Sci. Rep. 12 3720Google Scholar

    [13]

    Soriano M C, Ortín S, Brunner D, Larger L, Mirasso C R, Fischer I, Pesquera L 2013 Opt. Express 21 12Google Scholar

    [14]

    Duport F, Schneider B, Smerieri A, Haelterman M, Massar S 2012 Opt. Express 20 22783Google Scholar

    [15]

    Genty G, Salmela L, Dudley J M, Brunner D, Kokhanovskiy, Kobtsev S, Turitsyn S K 2020 Nat. Photonics 15 91Google Scholar

    [16]

    花飞, 方捻, 王陆唐 2019 物理学报 68 224205Google Scholar

    Hua F, Fang N, Wang L T 2019 Acta Phys. Sin. 68 224205Google Scholar

    [17]

    刘奇, 李璞, 开超, 胡 春强, 蔡强, 张建国, 徐兵杰 2021 物理学报 70 154209Google Scholar

    Liu Q, Li P, Kai C, Hu C Q, Cai Q, Zhang J G, Xu B J 2021 Acta Phys. Sin. 70 154209Google Scholar

    [18]

    Argyris A, Schwind J, Fischer I 2021 Sci. Rep 11 6701Google Scholar

    [19]

    Tanaka G, Yamane T, Héroux J B, Nakane R, Kanazawa N, Takeda S, Numata H, Nakano D, Hirose A 2019 Neural Networks 115 100Google Scholar

    [20]

    Lugnan A, Katumba A, Laporte F, Freiberger M, Bienstman P 2020 APL Photonics 5 020901Google Scholar

    [21]

    Cai Q, Guo Y, Li P, Bogris A, Wang Y 2021 Photonics Res 9 14Google Scholar

    [22]

    Brunner D, Soriano M C, Mirasso C R, Fischer I 2013 Nat. Commun 4 1364Google Scholar

    [23]

    Nguimdo R M, Verschaffelt G, Danckaert J, Guy V D S 2015 IEEE Trans. Neural Networks Learn. Syst 26 3301Google Scholar

    [24]

    Vatin J, Rontani D, Sciamanna M 2018 Opt. Lett 43 4497Google Scholar

    [25]

    Nguimdo R M, Erneux T 2019 Opt. Lett 44 49Google Scholar

    [26]

    Bogris A, Mesaritakis C, Deligiannidis S, Li P 2020 IEEE J. Sel. Top. Quantum Electron 27 1Google Scholar

    [27]

    Guo X X, Xiang S Y, Zhang Y H, Lin L, Wen A J, Hao Y 2020 IEEE J. Sel. Top. Quantum Electron 26 1Google Scholar

    [28]

    Huang Y, Zhou P, Yang Y, Li N Q 2021 Opt. Lett 46 6035Google Scholar

    [29]

    Kuriki Y, Nakayama J, Takano K, Uchia A 2018 Opt. Express 26 5777Google Scholar

    [30]

    Wang D, Wang L, Zhao T, Gao H, Wang Y, Chen X, Wang A 2017 Opt. Express 25 10911Google Scholar

    [31]

    Argyris A, Bueno J, Fischer I 2018 Sci. Rep 8 8487Google Scholar

    [32]

    Hou Y S, Xia G Q, Yang W Y, Wang D, Jayaprasath E, Jiang Z F, Hu C X, Wu Z M 2018 Opt. Express 26 10211Google Scholar

    [33]

    Lang R, Kobayashi K 1980 IEEE J. Quantum Electron 16 347Google Scholar

    [34]

    Ohtsubo J 2017 Springer Series in Optical Sciences (USA)

    [35]

    Berre M L, Ressayre E, Talleta A, Gibbs H M, Kaplan D L, Rose M H 1987 Phys. Rev. A 35 4020Google Scholar

    [36]

    Jaeger H 2002 Conference and Workshop on Neural Information Processing Systems Vancouver Canada, Nips December 9–14, 2002 p609

    [37]

    Yue D Z, Wu Z M, Hou Y S, Hu C X, Xia G Q 2021 IEEE Photonics J 13 1Google Scholar

    [38]

    Estébanez I, Schwind J, Fischer I, Argyris A 2020 Nanophotonics 9 4163Google Scholar

  • [1] 方捻, 钱若兰, 王帅. 基于偏振动力学的全光储备池计算系统. 物理学报, 2023, 72(21): 214205. doi: 10.7498/aps.72.20230722
    [2] 刘奇, 李璞, 开超, 胡春强, 蔡强, 张建国, 徐兵杰. 基于时延光子储备池计算的混沌激光短期预测. 物理学报, 2021, 70(15): 154209. doi: 10.7498/aps.70.20210355
    [3] 周英, 谢双媛, 许静平. 磁-腔量子电动力学系统中压缩驱动导致的两体与三体纠缠. 物理学报, 2020, 69(22): 220301. doi: 10.7498/aps.69.20200838
    [4] 花飞, 方捻, 王陆唐. 半导体激光器储备池计算系统的工作点选取方法. 物理学报, 2019, 68(22): 224205. doi: 10.7498/aps.68.20191039
    [5] 孔祥宇, 朱垣晔, 闻经纬, 辛涛, 李可仁, 龙桂鲁. 核磁共振量子信息处理研究的新进展. 物理学报, 2018, 67(22): 220301. doi: 10.7498/aps.67.20180754
    [6] 王春妮, 王亚, 马军. 基于亥姆霍兹定理计算动力学系统的哈密顿能量函数. 物理学报, 2016, 65(24): 240501. doi: 10.7498/aps.65.240501
    [7] 张宏, 丁炯, 童勤业, 程千流. 双耳幅值差确定声源方向的神经信息处理机理研究. 物理学报, 2015, 64(18): 188701. doi: 10.7498/aps.64.188701
    [8] 陈雪, 刘晓威, 张可烨, 袁春华, 张卫平. 腔光力学系统中的量子测量. 物理学报, 2015, 64(16): 164211. doi: 10.7498/aps.64.164211
    [9] 杨显杰, 陈建军, 夏光琼, 吴加贵, 吴正茂. 主副垂直腔面发射激光器动力学系统混沌输出的时延特征及带宽分析. 物理学报, 2015, 64(22): 224213. doi: 10.7498/aps.64.224213
    [10] 文瑞娟, 杜金锦, 李文芳, 李刚, 张天才. 内腔多原子直接俘获的强耦合腔量子力学系统的构建. 物理学报, 2014, 63(24): 244203. doi: 10.7498/aps.63.244203
    [11] 卢道明. 腔量子电动力学系统中耦合三原子的纠缠特性. 物理学报, 2014, 63(6): 060301. doi: 10.7498/aps.63.060301
    [12] 张旭东, 朱萍, 谢小平, 何国光. 混沌神经网络的动态阈值控制. 物理学报, 2013, 62(21): 210506. doi: 10.7498/aps.62.210506
    [13] 楼智美. 二阶非线性耦合动力学系统守恒量的扩展Prelle-Singer求法与对称性研究. 物理学报, 2010, 59(2): 719-723. doi: 10.7498/aps.59.719
    [14] 王 坤. 二端面转轴相对转动非线性动力学系统的稳定性与近似解. 物理学报, 2005, 54(12): 5530-5533. doi: 10.7498/aps.54.5530
    [15] 宋克慧. 利用Λ型原子与双模腔场的相互作用进行量子信息处理. 物理学报, 2005, 54(10): 4730-4735. doi: 10.7498/aps.54.4730
    [16] 马 晶, 谭立英, 冉启文. 小波分析在光学信息处理中的应用. 物理学报, 1999, 48(7): 1223-1229. doi: 10.7498/aps.48.1223
    [17] 徐云, 张建峡, 杜世培. 动力学系统中非线性项的跳跃随机性. 物理学报, 1991, 40(1): 33-38. doi: 10.7498/aps.40.33
    [18] 庄松林, 郑权. 部分相干信息处理中的逆源问题. 物理学报, 1985, 34(4): 439-446. doi: 10.7498/aps.34.439
    [19] 母国光, 庄松林. 用白光信息处理作褪色透明片的彩色复原. 物理学报, 1981, 30(6): 841-848. doi: 10.7498/aps.30.841
    [20] 杨振寰, 庄松林, 赵天欣. 彩色图象的光学信息处理. 物理学报, 1981, 30(1): 57-65. doi: 10.7498/aps.30.57
计量
  • 文章访问数:  4088
  • PDF下载量:  86
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-04-22
  • 修回日期:  2022-05-29
  • 上网日期:  2022-09-27
  • 刊出日期:  2022-10-05

/

返回文章
返回