Biomimetic acoustic perception via chip-scale dual-soliton microcombs
Teng Tan, Xinyue He, Bing Chang, Xuhan Guo, Heng Zhou, Yong Geng, Yu‐Fei Wu, Yupei Liang, Zeping Wang, Yongjun Huang, Yingzhan Yan, Si‐Qin Ge, Yikai Su, Chee Wei Wong, Baicheng Yao
- 发表年份
- 2025
- 引用次数
- 11
- 访问权限
- 开放获取
摘要
Abstract Acoustic perception is a fairly basic but extraordinary feature in nature, relying on multidimensional signal processing for detection, localization, and recognition. Replicating this capability in compact artificial systems, however, remains a formidable challenge due to limitations in scalability, sensitivity, and integration. Here, imitating the auditory system of insects, we introduce an opto-acoustic perception paradigm using fully-stabilized dual-soliton microcombs. By integrating digitally stabilized on-chip dual-microcombs, silicon optoelectronics and bionic fiber-microphone arrays on a single platform, we achieve parallelized interrogation of over 100 sensors. Leveraging the low-noise, multi-channel coherence of fully-stabilized soliton microcombs, this synergy enables ultra-sensitive detection of 29.3 nPa/Hz 1/2 , sub centimeter precise localization, real-time tracking and identification for versatile acoustic targets. Bridging silicon photonics, optical fiber sensing and intelligent signal processing in a chiplet microsystem, our scheme delivers out-of-lab deployable capability on autonomous robotics. This work not only deepens the understanding of frequency comb science, but also establishes a concept of dual-comb-driven sensor networks as a scalable foundation for next-generation opto-acoustic intelligence.
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