Enhancing Uniformity, Read Voltage Margin, and Retention in Three-Dimensional and Self-Rectifying Vertical Pt/Ta2O5/Al2O3/TiN Memristors

被引:3
作者
Park, Tae Won [1 ,2 ]
Moon, Jiwon [1 ,2 ]
Shin, Dong Hoon [1 ,2 ]
Kim, Hae Jin [3 ]
Kim, Seung Soo [1 ,2 ]
Cho, Jea Min [1 ,2 ]
Park, Hyungjun [1 ,2 ]
Woo, Kyung Seok [1 ,2 ]
Kim, Dong Yun [1 ,2 ]
Cheong, Sunwoo [1 ,2 ]
Song, Haewon [1 ,2 ]
Shin, Jong Hoon [1 ,2 ]
Lee, Soo Hyung [1 ,2 ]
Ghenzi, Nestor [1 ,2 ,4 ,5 ]
Hwang, Cheol Seong [1 ,2 ]
机构
[1] Seoul Natl Univ, Coll Engn, Dept Mat Sci & Engn, Seoul 08826, South Korea
[2] Seoul Natl Univ, Coll Engn, Interuniv Semicond Res Ctr, Seoul 08826, South Korea
[3] Myongji Univ, Dept Mat Sci & Engn, Yongin 17058, South Korea
[4] Univ Avellaneda UNDAV, Buenos Aires, Buenos Aires, Argentina
[5] Consejo Nacl Invest Cient & Tecn CONICET, B1870, Buenos Aires, Argentina
基金
新加坡国家研究基金会;
关键词
charge-trap; self-rectifying; memristor; V-RRAM; retention; uniformity; FORMING-FREE; RESISTANCE; MEMORY;
D O I
10.1021/acsami.4c15598
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
This study introduces a Ta2O5-based self-rectifying memristor (SRM) with an Al2O3 interfacial layer adopted to improve switching uniformity, read voltage margin, and long-term retention. The Pt/Ta2O5/Al2O3/TiN (PTAT) device exhibits a 105 rectification ratio, 104 on/off ratio, 2 x 106 endurance, and retention of 104 s at 150 degrees C. A 3-layer 4 x 4 vertical resistive random access memory structure exhibits uniform switching parameters. The coefficient of variation (CV) for device-to-device measurements is 0.23 for the low resistance state (LRS) and 0.22 for the high resistance state (HRS), while for cycle-to-cycle measurements, the CV is 0.38 for the LRS and 0.11 for the HRS. Finally, the present study demonstrates the superior performance of the PTAT devices in the context of hardware-aware training for a fully connected neural network implementation. These advancements position the PTAT device as a promising candidate for high-density three-dimensional storage class memory and low-power neural networks, offering the consistent performance and reliability necessary for future high-density storage applications.
引用
收藏
页码:65046 / 65057
页数:12
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