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Integrated bionic LiDAR for adaptive 4D machine vision.

Ruixuan Chen1, Yichen Wu1, Ke Zhang2

  • 1State Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing, China.

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|December 23, 2025
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Summary
This summary is machine-generated.

This study introduces a novel retina-inspired LiDAR system with dynamic gazing. This chip-scale LiDAR achieves beyond-retinal resolution, offering an energy-efficient solution for autonomous systems.

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Area of Science:

  • Photonics
  • Machine Vision
  • Biomedical Engineering

Background:

  • Current machine vision systems lack the adaptive focusing of the human eye.
  • Scaling traditional sensing methods for high resolution is costly and power-intensive.
  • Existing systems struggle to emulate the eye's efficiency and resolution.

Purpose of the Study:

  • To develop a chip-scale LiDAR architecture inspired by the human retina.
  • To overcome the limitations of rigid parallel-sensing mechanisms in machine vision.
  • To enable dynamic gazing and efficient resource allocation for high-resolution sensing.

Main Methods:

  • Developed a retina-inspired chip-scale LiDAR architecture.
  • Integrated an agile external cavity laser with reconfigurable electro-optic frequency combs.
  • Enabled coherent LiDAR with parallel, reconfigurable channels for dynamic resource allocation.

Main Results:

  • Achieved real-time 4D imaging with 0.012° beyond-retinal resolution.
  • Demonstrated dynamic gazing capability, allocating sensing resources to critical regions.
  • Showcased hardware efficiency and scalable chip-level manufacturability.

Conclusions:

  • The developed LiDAR technology offers a path toward lightweight, high-performance perception for autonomous systems.
  • The retina-inspired design overcomes the bottlenecks of current machine vision.
  • This technology enables beyond-retinal resolution and dynamic sensing capabilities.