Stacked One-Selector-One-Resistive Memory Crossbar Array With High Nonlinearity and On-Current Density for the Neuromorphic Applications

被引:14
作者
Woo, Hyo Cheon [1 ,2 ]
Kim, Jihun [1 ,2 ]
Lee, Sunwoo [1 ,2 ]
Kim, Hae Jin [1 ,2 ,3 ]
Hwang, Cheol Seong [1 ,2 ]
机构
[1] Seoul Natl Univ, Dept Mat Sci & Engn, Gwanak Ro 1, Seoul 08826, South Korea
[2] Seoul Natl Univ, Interuniv Semicond Res Ctr, Gwanak Ro 1, Seoul 08826, South Korea
[3] Univ Suwon, Dept Elect Mat Engn, Hwaseong Si 18323, South Korea
基金
新加坡国家研究基金会;
关键词
crossbar arrays; neural networks; nonlinear selectors; one selector-one resistive switching memories; MEMRISTOR; ELECTRODES; DEVICES; DIODE;
D O I
10.1002/aelm.202200656
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
A crossbar array using resistive switching random-access memory requires a selector device to prevent leakage current. However, the high current flow during the electroforming and first reset process (switching from a low resistance state to a high resistance state) can degrade the selector device. Ruthenium dioxide, a conducting oxide electrode with low oxygen affinity preventing excess oxygen vacancy in a dielectric, is used as a selector electrode to acquire a TiO2-based nonlinear selector that endures a high current flow. The selector shows high nonlinearity (approximate to 7 x 10(4)), high forward and reverse current ratio (approximate to 5.5 x 10(2)), and sufficient endurance (>10(7)) in the one selector-one resistive switching memory (1S1R) structure, where the HfO2 comprises the resistive switching memory. 9 x 9 crossbar array composed of the 1S1R device is used as neuromorphic hardware to classify simple characters by offline supervised learning. Further classification simulation of the rescaled Modified National Institute of Standards and Technology dataset shows 95.77% accuracy with achievable array structure.
引用
收藏
页数:13
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