A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding

Fakun Wang, Fangchen Hu, Mingjin Dai, Song Zhu, Fangyuan Sun, Ruihuan Duan, Chongwu Wang, Jiayue Han, Wenjie Deng, Wenduo Chen, Ming Ye, Song Han, Bo Qiang, Yuhao Jin, Yunda Chua, Nan Chi, Shaohua Yu, Donguk Nam, Sang Hoon Chae, Zheng Liu and Qi Jie Wang ()
Additional contact information
Fakun Wang: Nanyang Technological University
Fangchen Hu: Nanyang Technological University
Mingjin Dai: Nanyang Technological University
Song Zhu: Nanyang Technological University
Fangyuan Sun: Nanyang Technological University
Ruihuan Duan: Nanyang Technological University
Chongwu Wang: Nanyang Technological University
Jiayue Han: Nanyang Technological University
Wenjie Deng: Nanyang Technological University
Wenduo Chen: Nanyang Technological University
Ming Ye: Nanyang Technological University
Song Han: Nanyang Technological University
Bo Qiang: Nanyang Technological University
Yuhao Jin: Nanyang Technological University
Yunda Chua: Nanyang Technological University
Nan Chi: Fudan University
Shaohua Yu: Peng Cheng Laboratory
Donguk Nam: Nanyang Technological University
Sang Hoon Chae: Nanyang Technological University
Zheng Liu: Nanyang Technological University
Qi Jie Wang: Nanyang Technological University

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract Infrared machine vision system for object perception and recognition is becoming increasingly important in the Internet of Things era. However, the current system suffers from bulkiness and inefficiency as compared to the human retina with the intelligent and compact neural architecture. Here, we present a retina-inspired mid-infrared (MIR) optoelectronic device based on a two-dimensional (2D) heterostructure for simultaneous data perception and encoding. A single device can perceive the illumination intensity of a MIR stimulus signal, while encoding the intensity into a spike train based on a rate encoding algorithm for subsequent neuromorphic computing with the assistance of an all-optical excitation mechanism, a stochastic near-infrared (NIR) sampling terminal. The device features wide dynamic working range, high encoding precision, and flexible adaption ability to the MIR intensity. Moreover, an inference accuracy more than 96% to MIR MNIST data set encoded by the device is achieved using a trained spiking neural network (SNN).

Date: 2023
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DOI: 10.1038/s41467-023-37623-5

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