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The Retina01:32

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The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
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Retina-Inspired 2D Semiconductor NIR Sensor with PRO Architecture for Photodetection.

Xingchao Zhang1,2,3, Chunsheng Chen4,5, Lanying Zhou2

  • 1School of Physics and Advanced Energy, Henan University of Technology, Zhengzhou, Henan 450001, China.

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|December 17, 2025
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Summary

Researchers developed novel neuro-biosensors for LiDAR systems. These photosensitive ring oscillators (PROs) use unique nanoparticles to improve near-infrared light detection, enabling real-time, high-accuracy autonomous driving sensing.

Keywords:
2D semiconductoroptical-frequency conversionphotosensitive ring oscillatorup-conversion luminescence

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Area of Science:

  • Materials Science and Engineering
  • Neuromorphic Engineering
  • Photonics and Optoelectronics

Background:

  • Light detection and ranging (LiDAR) is crucial for autonomous driving, requiring efficient near-infrared (NIR) photodetectors.
  • Current CMOS-based LiDAR systems face computational inefficiency, high latency, and significant power demands due to the von Neumann architecture.
  • Neuromorphic sensors offer integrated sensing and computation but often require additional analog-to-digital converters (ADCs).

Purpose of the Study:

  • To introduce a novel photosensitive ring oscillator (PRO) based visual afferent neuro-biosensor system for LiDAR applications.
  • To overcome the limitations of existing LiDAR detection systems by eliminating the need for ADCs and enhancing system simplicity.
  • To achieve high-accuracy, real-time environmental sensing, particularly in challenging low-light conditions.

Main Methods:

  • Fabrication of PROs utilizing monolayer MoS2 as channels.
  • Decoration of MoS2 channels with Nd3+/Yb3+/Er3+ tridoped NaYF4 up-conversion nanoparticles (UCNPs) for NIR sensitivity.
  • Integration and simulation of a VoxelNet neural network for image preprocessing and environmental information extraction.

Main Results:

  • Demonstration of NIR-triggered oscillation frequency modulation in the PRO sensors.
  • The PRO architecture successfully avoids the need for ADCs, leading to enhanced noise immunity and system simplification.
  • Simulated VoxelNet preprocessing achieved high recognition accuracy, especially under low-light conditions, validating the system's effectiveness.

Conclusions:

  • The developed PRO-based neuro-biosensors offer a promising, ADC-free approach for LiDAR applications.
  • This technology enhances noise immunity and system simplicity, addressing key limitations of current LiDAR systems.
  • The proposed system represents a new paradigm for real-time, high-accuracy LiDAR sensing, crucial for advancing autonomous driving technology.