IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v199y2025ip1s096007792500640x.html

Time delay reservoir computing based on mutually coupled add-drop microring resonators

Author

Listed:
  • Li, Lili
  • Xie, Yiyuan
  • Jiang, Xiao
  • Su, Ye
  • Ye, Yichen
  • Li, Zelin
  • Tang, Yuhan

Abstract

Add-drop silicon microring resonator (MRR) has notable advantages in low power consumption, light weight and scalability, making it one of the research hotspots in optical reservoir computing (ORC) and optical chip. In this paper, for the first time, we propose a novel nonlinear dynamic system using mutually coupled (MC) add-drop MRRs with clockwise and counter-clockwise optical injection. Utilizing the proposed dynamical model, which is grounded in modified nonlinear dynamic equations incorporating coupled mode theory (CMT), we further construct an ORC system. Different dynamic behaviors and internal physical mechanisms, affected by key parameters, are analyzed in detail through bifurcation diagrams. Based on this, the effects of key parameters including injection strength, pump power, and injection delay time on the performance of ORC are detailedly analyzed in results. Through comprehensive analysis and optimization, the proposed ORC can achieve the normalized mean square error (NMSE) of 0.4% for the prediction task and the symbol error rate (SER) of 0.2% with SNR of 24 dB for nonlinear channel equalization. By analyzing the effects of the system output state, the number of virtual node, and scaling factor on the above tasks, we achieve remarkable recognition accuracies, attaining 99% on the MNIST dataset and 86.8% on the Fashion-MNIST dataset. The results and analysis underscore the importance of mastering the dynamic mechanism of the proposed model to achieve optimal application performance for constructed ORC systems. An in-depth understanding of the proposed model offers valuable insights and inspiration for the subsequent development of integrated topologies.

Suggested Citation

  • Li, Lili & Xie, Yiyuan & Jiang, Xiao & Su, Ye & Ye, Yichen & Li, Zelin & Tang, Yuhan, 2025. "Time delay reservoir computing based on mutually coupled add-drop microring resonators," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
  • Handle: RePEc:eee:chsofr:v:199:y:2025:i:p1:s096007792500640x
    DOI: 10.1016/j.chaos.2025.116627
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007792500640X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2025.116627?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Author Correction: Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-1, December.
    2. Daniel J. Gauthier & Erik Bollt & Aaron Griffith & Wendson A. S. Barbosa, 2021. "Next generation reservoir computing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    3. Wang, Tao & Zhou, Hanxu & Fang, Qing & Han, Yanan & Guo, Xingxing & Zhang, Yahui & Qian, Chao & Chen, Hongsheng & Barland, Stéphane & Xiang, Shuiying & Lippi, Gian Luca, 2024. "Reservoir computing-based advance warning of extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. L. Appeltant & M.C. Soriano & G. Van der Sande & J. Danckaert & S. Massar & J. Dambre & B. Schrauwen & C.R. Mirasso & I. Fischer, 2011. "Information processing using a single dynamical node as complex system," Nature Communications, Nature, vol. 2(1), pages 1-6, September.
    6. Yanan Zhong & Jianshi Tang & Xinyi Li & Bin Gao & He Qian & Huaqiang Wu, 2021. "Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    7. Yuan, Xin & Jiang, Lin & Yan, Lianshan & Li, Songsui & Zhang, Liyue & Yi, Anlin & Pan, Wei & Luo, Bin, 2024. "The optoelectronic reservoir computing system based on parallel multi-time-delay feedback loops for time-series prediction and optical performance monitoring," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    8. Kristof Vandoorne & Pauline Mechet & Thomas Van Vaerenbergh & Martin Fiers & Geert Morthier & David Verstraeten & Benjamin Schrauwen & Joni Dambre & Peter Bienstman, 2014. "Experimental demonstration of reservoir computing on a silicon photonics chip," Nature Communications, Nature, vol. 5(1), pages 1-6, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cai, Deyu & Mu, Penghua & Huang, Yu & Zhou, Pei & Li, Nianqiang, 2024. "A reinforced reservoir computer aided by an external asymmetric dual-path-filtering cavity laser," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    2. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Dongliang Wang & Yikun Nie & Gaolei Hu & Hon Ki Tsang & Chaoran Huang, 2024. "Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Yang, Liangli & Pang, Siqing & Zhang, Yutai & Zhou, Yihua & Sun, Xinyue & Kong, Yixiu & Zhang, Yi-Cheng, 2025. "Improved next generation reservoir computing with time decay factor and kernel function," Chaos, Solitons & Fractals, Elsevier, vol. 198(C).
    5. Jangsaeng Kim & Eun Chan Park & Wonjun Shin & Ryun-Han Koo & Chang-Hyeon Han & He Young Kang & Tae Gyu Yang & Youngin Goh & Kilho Lee & Daewon Ha & Suraj S. Cheema & Jae Kyeong Jeong & Daewoong Kwon, 2024. "Analog reservoir computing via ferroelectric mixed phase boundary transistors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Lukas Körber & Christopher Heins & Tobias Hula & Joo-Von Kim & Sonia Thlang & Helmut Schultheiss & Jürgen Fassbender & Katrin Schultheiss, 2023. "Pattern recognition in reciprocal space with a magnon-scattering reservoir," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    7. Li, Lili & Xie, Yiyuan & Jiang, Xiao & Su, Ye & Ye, Yichen & Tang, Yuhan & Zhou, Wenjun, 2025. "Deep optical reservoir computing based on microring resonators for nonlinear channel equalization and image classification," Chaos, Solitons & Fractals, Elsevier, vol. 201(P2).
    8. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. Xiangpeng Liang & Yanan Zhong & Jianshi Tang & Zhengwu Liu & Peng Yao & Keyang Sun & Qingtian Zhang & Bin Gao & Hadi Heidari & He Qian & Huaqiang Wu, 2022. "Rotating neurons for all-analog implementation of cyclic reservoir computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    10. Lina Jaurigue & Kathy Lüdge, 2022. "Connecting reservoir computing with statistical forecasting and deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-3, December.
    11. Yang, J. & Primo, E. & Aleja, D. & Criado, R. & Boccaletti, S. & Alfaro-Bittner, K., 2022. "Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    12. Minati, Ludovico & Mancinelli, Mattia & Frasca, Mattia & Bettotti, Paolo & Pavesi, Lorenzo, 2021. "An analog electronic emulator of non-linear dynamics in optical microring resonators," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    13. Deng, Yue & Zhang, Shuting & Yuan, Fang & Li, Yuxia & Wang, Guangyi, 2025. "Reservoir computing system using discrete memristor for chaotic temporal signal processing," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    14. Zhuohui Liu & Qinghua Zhang & Donggang Xie & Mingzhen Zhang & Xinyan Li & Hai Zhong & Ge Li & Meng He & Dashan Shang & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2023. "Interface-type tunable oxygen ion dynamics for physical reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    15. Zhiyuan Li & Zhongshao Li & Wei Tang & Jiaping Yao & Zhipeng Dou & Junjie Gong & Yongfei Li & Beining Zhang & Yunxiao Dong & Jian Xia & Lin Sun & Peng Jiang & Xun Cao & Rui Yang & Xiangshui Miao & Ron, 2024. "Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    16. Zheng-Meng Zhai & Mohammadamin Moradi & Ling-Wei Kong & Bryan Glaz & Mulugeta Haile & Ying-Cheng Lai, 2023. "Model-free tracking control of complex dynamical trajectories with machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    17. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    18. Anirban Chowdhury & Anshul Rasyotra & Harikrishnan Ravichandran & Denesh Kumar Manoharan & Yongwen Sun & Chen Chen & Joan M. Redwing & Yang Yang & Saptarshi Das, 2025. "3D Integration of functionally diverse 2D materials for optoelectronic reservoir computing," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    19. Sanchez, Luciano & Costa, Nahuel & Couso, Ines, 2025. "Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    20. Laura E. Suárez & Agoston Mihalik & Filip Milisav & Kenji Marshall & Mingze Li & Petra E. Vértes & Guillaume Lajoie & Bratislav Misic, 2024. "Connectome-based reservoir computing with the conn2res toolbox," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:199:y:2025:i:p1:s096007792500640x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.