Статья

Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing

Yaxin HouMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaLi YiKey Laboratory of Computer Vision and Systems (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaZhicheng ZhangMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaJiaqiang LiCenter for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, ChinaDe-Han QiKey Laboratory of Computer Vision and Systems (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaXu‐Dong ChenMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaJingjing WangMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaBin‐Wei YaoMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaMei‐Xi YuMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaTong‐Bu LuMOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, ChinaJin ZhangCenter for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China

2020en

ABI

Аннотация

Optoelectronic synapses integrating synaptic and optical-sensing functions exhibit large advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy-efficient way. However, electric stimulation is still essential for existing optoelectronic synapses to realize bidirectional weight-updating, restricting the processing speed, bandwidth, and integration density of the devices. Herein, a two-terminal optical synapse based on a wafer-scale pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure is proposed that can emulate both the excitatory and inhibitory synaptic behaviors in an optical pathway. The simple device architecture and low-dimensional features of the heterostructure endow the optical synapse with robust flexibility for wearable electronics. This optical synapse features a linear and symmetric conductance-update trajectory with numerous conductance states and low noise, which facilitates the demonstration of accurate and effective pattern recognition with a strong fault-tolerant capability even at bending states. A series of logic functions and associative learning capabilities have been demonstrated by the optical synapses in optical pathways, significantly enhancing the information processing capability for neuromorphic computing. Moreover, an integrated visible information sensing memory processing system based on the optical synapse array is constructed to perform real-time detection, in situ image memorization, and distinction tasks. This work is an important step toward the development of optogenetics-inspired neuromorphic computing and adaptive parallel processing networks for wearable electronics.

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Идентификаторы

DOI: 10.1021/acsnano.0c08921

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar