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

来源：

2018 IEEE SYMPOSIUM ON VLSI TECHNOLOGY | 2018年

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

页码：103 / 104

页数：2

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