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

Bitao Shen, Haowen Shu (), Weiqiang Xie, Ruixuan Chen, Zhi Liu, Zhangfeng Ge, Xuguang Zhang, Yimeng Wang, Yunhao Zhang, Buwen Cheng, Shaohua Yu, Lin Chang () and Xingjun Wang ()
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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
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
Shaohua Yu: Peking University
Lin Chang: Peking University
Xingjun Wang: Peking University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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

Date: 2023
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DOI: 10.1038/s41467-023-40152-w

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