Improving linearity by introducing Al in HfO2 as a memristor synapse device

被引：112

作者：

Chandrasekaran, Sridhar ^{[1
]}

Simanjuntak, Firman Mangasa ^{[2
]}

Saminathan, R. ^{[3
]}

Panda, Debashis ^{[4
]}

Tseng, Tseung-Yuen ^{[5
,6
]}

机构：

[1] Natl Chiao Tung Univ, Dept Elect Engn & Comp Sci, Hsinchu 30010, Taiwan

[2] Tohoku Univ, Adv Inst Mat Res, WPI, Sendai, Miyagi 9808577, Japan

[3] Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30010, Taiwan

[4] Natl Inst Sci & Technol, Elect Engn & Phys Dept, Berhampur 761008, Orissa, India

[5] Natl Chiao Tung Univ, Dept Elect Engn, Hsinchu 30010, Taiwan

[6] Natl Chiao Tung Univ, Inst Elect, Hsinchu 30010, Taiwan

来源：

NANOTECHNOLOGY | 2019年 / 30卷 / 44期

关键词：

artificial synaptic; neuromorphic; resistive switching; memristor; RESISTIVE MEMORY; OXIDE; RRAM; POTENTIATION; DEPRESSION; FILMS;

D O I：

10.1088/1361-6528/ab3480

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.

引用

页数：9

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