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Harnessing microcomb-based parallel chaos for random number generation and optical decision making

Author

Listed:
  • Bitao Shen

    (Peking University)

  • Haowen Shu

    (Peking University)

  • Weiqiang Xie

    (Shanghai Jiao Tong University)

  • Ruixuan Chen

    (Peking University)

  • Zhi Liu

    (Institute of Semiconductors, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhangfeng Ge

    (Peking University Yangtze Delta Institute of Optoelectronics)

  • Xuguang Zhang

    (Peking University)

  • Yimeng Wang

    (Peking University)

  • Yunhao Zhang

    (Peking University)

  • Buwen Cheng

    (Institute of Semiconductors, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Shaohua Yu

    (Peking University
    Peng Cheng Laboratory)

  • Lin Chang

    (Peking University
    Peking University)

  • Xingjun Wang

    (Peking University
    Peking University Yangtze Delta Institute of Optoelectronics
    Peng Cheng Laboratory
    Peking University)

Abstract

Optical chaos is vital for various applications such as private communication, encryption, anti-interference sensing, and reinforcement learning. Chaotic microcombs have emerged as promising sources for generating massive optical chaos. However, their inter-channel correlation behavior remains elusive, limiting their potential for on-chip parallel chaotic systems with high throughput. In this study, we present massively parallel chaos based on chaotic microcombs and high-nonlinearity AlGaAsOI platforms. We demonstrate the feasibility of generating parallel chaotic signals with inter-channel correlation

Suggested Citation

  • Bitao Shen & Haowen Shu & Weiqiang Xie & Ruixuan Chen & Zhi Liu & Zhangfeng Ge & Xuguang Zhang & Yimeng Wang & Yunhao Zhang & Buwen Cheng & Shaohua Yu & Lin Chang & Xingjun Wang, 2023. "Harnessing microcomb-based parallel chaos for random number generation and optical decision making," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40152-w
    DOI: 10.1038/s41467-023-40152-w
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    1. Lin Chang & Weiqiang Xie & Haowen Shu & Qi-Fan Yang & Boqiang Shen & Andreas Boes & Jon D. Peters & Warren Jin & Chao Xiang & Songtao Liu & Gregory Moille & Su-Peng Yu & Xingjun Wang & Kartik Srinivas, 2020. "Ultra-efficient frequency comb generation in AlGaAs-on-insulator microresonators," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    2. Junqiu Liu & Guanhao Huang & Rui Ning Wang & Jijun He & Arslan S. Raja & Tianyi Liu & Nils J. Engelsen & Tobias J. Kippenberg, 2021. "High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Ferdinand Albert & Caspar Hopfmann & Stephan Reitzenstein & Christian Schneider & Sven Höfling & Lukas Worschech & Martin Kamp & Wolfgang Kinzel & Alfred Forchel & Ido Kanter, 2011. "Observing chaos for quantum-dot microlasers with external feedback," Nature Communications, Nature, vol. 2(1), pages 1-5, September.
    4. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 589(7840), pages 52-58, January.
    5. Apostolos Argyris & Dimitris Syvridis & Laurent Larger & Valerio Annovazzi-Lodi & Pere Colet & Ingo Fischer & Jordi García-Ojalvo & Claudio R. Mirasso & Luis Pesquera & K. Alan Shore, 2005. "Chaos-based communications at high bit rates using commercial fibre-optic links," Nature, Nature, vol. 438(7066), pages 343-346, November.
    6. Jiagui Wu & Shu-Wei Huang & Yongjun Huang & Hao Zhou & Jinghui Yang & Jia-Ming Liu & Mingbin Yu & Guoqiang Lo & Dim-Lee Kwong & Shukai Duan & Chee Wei Wong, 2017. "Mesoscopic chaos mediated by Drude electron-hole plasma in silicon optomechanical oscillators," Nature Communications, Nature, vol. 8(1), pages 1-7, August.
    7. Haowen Shu & Lin Chang & Yuansheng Tao & Bitao Shen & Weiqiang Xie & Ming Jin & Andrew Netherton & Zihan Tao & Xuguang Zhang & Ruixuan Chen & Bowen Bai & Jun Qin & Shaohua Yu & Xingjun Wang & John E. , 2022. "Microcomb-driven silicon photonic systems," Nature, Nature, vol. 605(7910), pages 457-463, May.
    8. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Publisher Correction: Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 591(7849), pages 13-13, March.
    9. Pablo Marin-Palomo & Juned N. Kemal & Maxim Karpov & Arne Kordts & Joerg Pfeifle & Martin H. P. Pfeiffer & Philipp Trocha & Stefan Wolf & Victor Brasch & Miles H. Anderson & Ralf Rosenberger & Kovendh, 2017. "Microresonator-based solitons for massively parallel coherent optical communications," Nature, Nature, vol. 546(7657), pages 274-279, June.
    10. Xingyuan Xu & Mengxi Tan & Bill Corcoran & Jiayang Wu & Andreas Boes & Thach G. Nguyen & Sai T. Chu & Brent E. Little & Damien G. Hicks & Roberto Morandotti & Arnan Mitchell & David J. Moss, 2021. "11 TOPS photonic convolutional accelerator for optical neural networks," Nature, Nature, vol. 589(7840), pages 44-51, January.
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