神经形态工程学
材料科学
计算机科学
冯·诺依曼建筑
光电子学
晶体管
人工神经网络
突触
逻辑门
杠杆(统计)
兴奋剂
人工智能
电气工程
电压
算法
神经科学
工程类
生物
操作系统
作者
B.S. Pei,Xuchen Han,Yan Wang,Jing Liu
出处
期刊:Small
[Wiley]
日期:2025-04-17
标识
DOI:10.1002/smll.202500184
摘要
Synaptic devices serve as the fundamental units of the brain-inspired neuromorphic computing architecture, which has been proposed to complement the drawback of von Neumann configuration in terms of computational efficiency. In this study, a dual-functional optoelectronic synaptic device is proposed based on the three-terminal MoTe2/h-BN transistor to seamlessly integrate both the synaptic and logic operation functions. The device can be switched between n- and p-type modes through ultraviolet (UV) light induced doping, allowing for versatile plasticity modulation strategies tailored to each operational mode. Comprehensive characterization of the synaptic behavior of the device reveals impressive stability and repeatability. The device is then explored to a virtual three-layered neural network array to classify the handwritten digit images from the Modified National Institute of Standards and Technology database, which achieves an accuracy of 95.4% and 94.2% for the n- and p-type modes, respectively, after 40 training cycles. The device also demonstrates its capability as optoelectronic logic gates, including "AND", "OR" and "XOR" under different gate bias. This multifaceted operation signifies a substantial advancement in the development of hybrid systems that leverage both synaptic and traditional logic functionalities, thereby enhancing the overall efficiency of data processing tasks.
科研通智能强力驱动
Strongly Powered by AbleSci AI