Integration of CeO2-Based Memristor with Vertically Aligned Nanocomposite Thin Film: Enabling Selective Conductive Filament Formation for High-Performance Electronic Synapses

被引:1
作者
Hu, Zedong [1 ]
Dou, Hongyi [2 ]
Zhang, Yizhi [2 ]
Shen, Jianan [2 ]
Ahmad, Laveeza [3 ]
Han, Shuyao [4 ]
Hollander, Elijah Gordon [2 ]
Lu, Juanjuan [2 ]
Zhang, Yifan [2 ]
Shang, Zhongxia [2 ]
Cao, Ye [3 ]
Huang, Jijie [4 ]
Wang, Haiyan [1 ,2 ]
机构
[1] Purdue Univ, Elmore Family Sch Elect & Comp Engn, W Lafayette, IN 47906 USA
[2] Purdue Univ, Sch Mat Engn, W Lafayette, IN 47906 USA
[3] Univ Texas Arlington, Dept Mat Sci & Engn, Arlington, TX 76019 USA
[4] Shenzhen Campus Sun Yat Sen Univ, Sch Mat, Shenzhen 518107, Peoples R China
基金
美国国家科学基金会;
关键词
CeO2; memristor; vertically alignednanocomposite; electronic synapse; neuromorphiccomputing; RESISTIVE SWITCHING MEMORY; SCHOTTKY-BARRIER; MECHANISM; DEVICE; DENSITY; FUTURE;
D O I
10.1021/acsami.4c10687
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
The CeO2-based memristor has attracted significant attention due to its intrinsic resistive switching (RS) properties, large on/off ratio, and great plasticity, making it a promising candidate for artificial synapses. However, significant challenges such as high power consumption and poor device reliability hinder its broad application in neuromorphic microchips. To tackle these issues, in this work, we design a novel bilayer (BL) memristor by integrating a CeO2-based memristor with a Co-CeO2 vertically aligned nanocomposite (VAN) layer and compare it with the single layer (SL) memristor. Preliminary electrical testing reveals that the BL memristor offers a reduced set/reset voltage (similar to 67% lower), a higher on/off ratio (similar to 5 x 10(2)), enhanced device reliability, and improved device-to-device variation compared to the SL memristor. Insight from COMSOL simulation, coupled with microstructural analysis, provides a comprehensive elucidation on how the VAN layer facilitates the selective conductive filament (CF) formation. Subsequently, the plasticity of the BL memristor is evaluated through long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-time-dependent plasticity (STDP). The spiking neural network (SNN) built upon the BL memristor achieves remarkable accuracy (similar to 94%) after only 12 iterations, underscoring its potential for high-performance neural networks.
引用
收藏
页码:64951 / 64962
页数:12
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