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Published on: August 17, 2011
High encoding-sensitivity vision sensor with complementary nonlinear neuromorphic computing
Quan Yang1, Chuanqing Wang2, Ziyang Shen3
1College of Integrated Circuits, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, China.
Nature Communications
|June 3, 2026
Summary
This study presents a novel neuromorphic vision sensor using a one-transistor-one-memristor design. It achieves broad light intensity adaptation by combining superlinear and sublinear encoding for brain-inspired computing.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Computer Vision
Background:
- Neuromorphic vision sensors are crucial for brain-inspired spiking neural networks.
- Adaptive sensitivity across wide light intensity ranges remains a significant challenge.
Purpose of the Study:
- To develop a neuromorphic vision sensor with enhanced adaptive sensitivity.
- To overcome limitations in dynamic range for light intensity encoding.
Main Methods:
- Utilized a one-transistor-one-memristor pixel structure.
- Integrated plasmonic volatile Ag/hBN/Au memristors for superlinear encoding.
- Employed MoS2 synaptic photodetectors and Ag/hBN/Au memristor neurons for sublinear encoding.
Main Results:
- Achieved high encoding sensitivity over a broad light intensity range by fusing complementary superlinear and sublinear encoding.
- Demonstrated high time-to-first-spike (TTFS) and rate encoding sensitivity in both high-brightness (superlinear) and dim light (sublinear) conditions.
- Successfully performed high-quality imaging, ice/land segmentation, and thickness prediction in polar environments.
Conclusions:
- The developed complementary neuromorphic vision sensor exhibits robust performance under challenging lighting conditions.
- This technology advances hardware for spiking neural networks by enabling adaptive sensitivity.
- The sensor shows potential for real-world applications requiring dynamic range imaging.
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