神经形态工程学
记忆电阻器
MNIST数据库
非阻塞I/O
材料科学
异质结
计算机科学
突触
人工神经网络
纳米技术
光电子学
电子工程
人工智能
化学
神经科学
工程类
生物
生物化学
催化作用
作者
Junlin Fang,Zhenhua Tang,Xi-Qi Li,Zhao-Yuan Fan,Yanping Jin,Qiu-Xiang Liu,Xin‐Gui Tang,Jingmin Fan,J. Gao,Jie Shang
摘要
A traditional von Neumann structure cannot adapt to the rapid development of artificial intelligence. To solve this issue, memristors have emerged as the preferred devices for simulating synaptic behavior and enabling neural morphological computations. In this work, Au/NiO/FTO and Au/MXene/NiO/FTO heterojunction memristors were prepared on FTO/glass by a sol-gel method. A comparative analysis was carried out to investigate the changes in electrical properties and synaptic behavior of the memristors upon the addition of MXene films. Au/MXene/NiO/FTO artificial synapses not only have smaller threshold voltage, larger switching ratio, and more intermediate conductivity states but also can simulate important synaptic behavior. The results show that the Au/MXene/NiO/FTO heterojunction memristor has better weight update linearity and excellent conductivity modulation behavior in addition to long data retention time characteristics. Utilizing a convolutional neural network architecture, the recognition accuracy of the MNIST and Fashion-MNIST datasets was improved to 96.8% and 81.7%, respectively, through the implementation of improved random adaptive algorithms. These results provide a feasible approach for combining MXene materials with metal oxides to prepare artificial synapses for the implementation of neuromorphic computing.
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