神经形态工程学
记忆电阻器
材料科学
超短脉冲
可扩展性
MNIST数据库
瓶颈
冯·诺依曼建筑
计算机科学
人工神经网络
光电子学
纳米技术
电子工程
人工智能
嵌入式系统
物理
工程类
激光器
光学
数据库
操作系统
作者
Zilong Dong,Qilin Hua,Jianguo Xi,Yuanhong Shi,Tianci Huang,Xinhuan Dai,Jianan Niu,Bingjun Wang,Zhong Lin Wang,Weiguo Hu
出处
期刊:Nano Letters
[American Chemical Society]
日期:2023-04-24
卷期号:23 (9): 3842-3850
被引量:52
标识
DOI:10.1021/acs.nanolett.3c00322
摘要
Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi2O2Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi2O2Se nanosheets are grown by configuring mica substrates face-down on the Bi2O2Se powder. Then, bipolar Bi2O2Se memristors are fabricated with excellent performance including ultrafast switching speed (<5 ns) and low-power consumption (<3.02 pJ). Moreover, synaptic plasticity, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are demonstrated in the Bi2O2Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi2O2Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.
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