Yaxin Hou; Li Yi; Zhicheng Zhang; Jiaqiang Li; De-Han Qi; Xu‐Dong Chen; Jingjing Wang; Bin‐Wei Yao; Mei‐Xi Yu; Tong‐Bu Lu; Jin Zhang · 2020 · ACS Nano
Paper
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.
Analysis
This paper introduces a novel two-terminal optical synapse based on a pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure that emulates synaptic behavior optically, enabling energy-efficient neuromorphic computing and integrated visual information processing.
Discovery
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