记忆电阻器
神经形态工程学
横杆开关
异质结
材料科学
冯·诺依曼建筑
光电子学
计算机科学
电压
离子
卷积神经网络
人工神经网络
纳米技术
物理
电子工程
电气工程
人工智能
工程类
电信
量子力学
操作系统
作者
Yesheng Li,Shuai Chen,Zhigen Yu,Sifan Li,Yao Xiong,Mer Er Pam,Yong‐Wei Zhang,Kah‐Wee Ang
标识
DOI:10.1002/adma.202201488
摘要
In-memory computing based on memristor arrays holds promise to address the speed and energy issues of the classical von Neumann computing system. However, the stochasticity of ions' transport in conventional oxide-based memristors imposes severe intrinsic variability, which compromises learning accuracy and hinders the implementation of neural network hardware accelerators. Here, these challenges are addressed using a low-voltage memristor array based on an ultrathin PdSeOx /PdSe2 heterostructure switching medium realized by a controllable ultraviolet (UV)-ozone treatment. A distinctively different ions' transport mechanism is revealed in the heterostructure that can confine the formation of conductive filaments, leading to a remarkable uniform switching with low set and reset voltage variability values of 4.8% and -3.6%, respectively. Moreover, convolutional image processing is further implemented using various crossbar kernels that achieve a high recognition accuracy of ≈93.4% due to the highly linear and symmetric analog weight update as well as multiple conductance states, manifesting its potential beyond von Neumann computing.
科研通智能强力驱动
Strongly Powered by AbleSci AI