High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network

Tian Zhang, Xin Guo, Pan Wang, Xinyi Fan, Zichen Wang, Yan Tong, Decheng Wang, Limin Tong and Linjun Li ()
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Tian Zhang: Zhejiang University
Xin Guo: Zhejiang University
Pan Wang: Zhejiang University
Xinyi Fan: Zhejiang University
Zichen Wang: Zhejiang University
Yan Tong: Zhejiang University
Decheng Wang: Zhejiang University
Limin Tong: Zhejiang University
Linjun Li: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10−17 J).

Date: 2024
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DOI: 10.1038/s41467-024-46867-8

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