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

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 pr...

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Detalles Bibliográficos
Autores principales: Wang, Fakun, Hu, Fangchen, Dai, Mingjin, Zhu, Song, Sun, Fangyuan, Duan, Ruihuan, Wang, Chongwu, Han, Jiayue, Deng, Wenjie, Chen, Wenduo, Ye, Ming, Han, Song, Qiang, Bo, Jin, Yuhao, Chua, Yunda, Chi, Nan, Yu, Shaohua, Nam, Donguk, Chae, Sang Hoon, Liu, Zheng, Wang, Qi Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079931/
https://www.ncbi.nlm.nih.gov/pubmed/37024508
http://dx.doi.org/10.1038/s41467-023-37623-5
Descripción
Sumario: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).