记忆电阻器
神经形态工程学
材料科学
卷积神经网络
记忆晶体管
铁电性
冯·诺依曼建筑
计算机科学
硅
光电子学
人工神经网络
电子工程
人工智能
电气工程
电阻随机存取存储器
工程类
电压
操作系统
电介质
作者
Gongjie Liu,Wei Wang,Zhenqiang Guo,Xi Jia,Zhen Zhao,Zhenyu Zhou,Jiangzhen Niu,Guang-Hua Duan,Xiaobing Yan
出处
期刊:Nanoscale
[The Royal Society of Chemistry]
日期:2023-01-01
卷期号:15 (31): 13009-13017
摘要
Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.
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