Integrated bionic LiDAR for adaptive 4D machine vision
The human eye achieves high resolution and energy efficiency through adaptive focusing on pertinent details. This natural capability sets a benchmark that current machine vision systems struggle to emulate due to rigid parallel-sensing mechanisms. Efforts to achieve retinal-level resolution through brute-force scaling of channels can lead to prohibitive cost, complexity, and power consumption. Here, we address this bottleneck with a retina-inspired chip-scale LiDAR architecture featuring dynamic gazing capability. By synergizing an agile external cavity laser with reconfigurable electro-optic frequency combs, our integrated photonic platform enables coherent LiDAR with parallel, reconfigurable channels, allocating sensing resources to critical regions without global oversampling. This hardware-efficient design enables real-time comb-based 4D imaging at 0.012° beyond-retinal resolution and supports 4D-plus imaging through cooperative sensing with a camera. With its beyond-retinal resolution, dynamic gazing capability, and scalable chip-level manufacturability, this technology offers a path toward lightweight, high-performance perception across aerial, terrestrial, and marine autonomous systems.
Authors
- Y. Zhou (ORCID: https://orcid.org/0000-0003-2886-0259)
- Zhangfeng Ge
- Bitao Shen (ORCID: https://orcid.org/0000-0002-3786-6815)
- Yimeng Wang
- L. P. Zhou
- Hanke Feng (ORCID: https://orcid.org/0000-0003-3579-5352)
- Chuxin Liu (ORCID: https://orcid.org/0000-0003-3887-1939)
- Ke Zhang (ORCID: https://orcid.org/0000-0003-1543-5587)
- Xuguang Zhang (ORCID: https://orcid.org/0000-0001-7763-0308)
- Haowen Shu (ORCID: https://orcid.org/0000-0002-5429-8661)
- Wencan Li (ORCID: https://orcid.org/0000-0003-1801-071X)
- Ruixuan Chen (ORCID: https://orcid.org/0000-0002-2925-9484)
- W N Xu
- Yichen Wu
- Zhaoxi CHEN
- Yikun Chen
- Dong Pan
- Cheng Wang
- Xingjun Wang
- Pengfei Cai
- Zihan Tao
Institutions
- King University (US)
- City University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Peking University (CN)
- Beijing Jiaotong University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-12-23
- DOI
- https://doi.org/10.1038/s41467-025-66529-7
- Primary Topic
- Advanced Optical Sensing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- City University of Hong Kong
- Croucher Foundation
- National Natural Science Foundation of China
- Research Grants Council, University Grants Committee
- Hong Kong University of Science and Technology
- Peking University
- National Postdoctoral Program for Innovative Talents