记忆电阻器
神经形态工程学
材料科学
纳米复合材料
电阻随机存取存储器
光电子学
纳米技术
电铸
突触重量
可靠性(半导体)
图层(电子)
电压
电子工程
人工神经网络
计算机科学
电气工程
功率(物理)
人工智能
物理
工程类
量子力学
作者
Zedong Hu,Hongyi Dou,Yizhi Zhang,Jianan Shen,L. Ahmad,Shuyao Han,Eric Hollander,Juanjuan Lu,Yifan Zhang,Zhongxia Shang,Ye Cao,Jijie Huang,Haiyan Wang
标识
DOI:10.1021/acsami.4c10687
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 (∼67% lower), a higher on/off ratio (∼5 × 102), 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 (∼94%) after only 12 iterations, underscoring its potential for high-performance neural networks.
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