神经形态工程学
材料科学
光电子学
非易失性存储器
石墨烯
异质结
电阻随机存取存储器
计算机数据存储
范德瓦尔斯力
堆栈(抽象数据类型)
记忆电阻器
纳米技术
计算机科学
电阻式触摸屏
人工神经网络
存储单元
电导
突触重量
纳米线
电子工程
感知器
备用电源
电压
工作(物理)
图层(电子)
晶体管
作者
Shiao Feng,Mingchen Xu,Gaoning Fan,Mengjiao Dong,Chun Hua Sun,Yonghui Zhang,Chao Fan
标识
DOI:10.1021/acsami.6c15978
摘要
Nonvolatile memories based on two-dimensional (2D) van der Waals (vdW) heterostructures have attracted considerable attention because of their atomic-scale thickness, low energy operation, and prolonged data storage capability. However, most reported vdW floating-gate memories employ a bottom-floating-gate configuration, which often leads to a limited on/off ratio due to charge leakage and substrate-induced disturbance. Herein, a top-floating-gate memory based on a graphene (Gr)/h-BN/SnS2 vdW heterostructure is demonstrated, in which the top graphene layer serves as the charge-trapping layer. The device exhibits excellent nonvolatile memory performance, including an on/off ratio of 106, a memory window of 50.8 V, a low standby current of 1 pA, and an extrapolated retention time of 10 years. Owing to the low hole-tunneling barrier between SnS2 and h-BN, the device achieves more than 50 progressively modulated conductance levels under a weak optical pulse of 0.74 nW·mm-2. Moreover, stable long-term potentiation and depression characteristics can be regulated by optical and electrical pulses. Based on these synaptic behaviors, a three-layer multilayer perceptron neural network using the floating-gate memory achieved a classification accuracy of 96% for handwritten digit recognition using the Modified National Institute of Standards and Technology (MNIST) dataset. This work presents a top-floating-gate architecture for 2D-material memories, providing a feasible strategy for future logic-in-memory and neuromorphic computing applications.
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