A Methodology to Improve Linearity of Analog RRAM for Neuromorphic Computing

被引:0
|
作者
Wu, Wei [1 ]
Wu, Huaqiang [1 ]
Gao, Bin [1 ]
Yao, Peng [1 ]
Zhang, Xiang [1 ]
Peng, Xiaochen [2 ]
Yu, Shimeng [2 ]
Qian, He [1 ]
机构
[1] Tsinghua Univ, Inst Microelect, Beijing, Peoples R China
[2] Arizona State Univ, Tempe, AZ USA
关键词
analog RRAM; synapse; online learning;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The conductance tuning linearity is an important parameter of analog RRAM for neuromorphic computing. This work presents a novel methodology to improve the conductance tuning linearity of the filamentary RRAM. An electro-thermal modulation layer is designed and introduced to control the distribution of electric field and temperature in the filament region. For the first time, a HfOx based RRAM is demonstrated with linear analog SET, linear analog RESET, 50ns speed, 10x analog tuning window, 100k Omega on-state resistance, and high temperature retention for multilevel states. The excellent performances of the analog RRAM devices enable high accuracy online learning in a neural network.
引用
收藏
页码:103 / 104
页数:2
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