HfO2/WO3 Heterojunction Structured Memristor for High-Density Storage and Neuromorphic Computing,Advanced Materials Technologies

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HfO2/WO3 Heterojunction Structured Memristor for High-Density Storage and Neuromorphic Computing
Advanced Materials Technologies ( IF 6.4 ) Pub Date : 2022-10-17 , DOI: 10.1002/admt.202201143
Qi Liu _{1,

2} , Song Gao _{1,

2} , Yang Li _{1,

2} , Wenjing Yue _{1,

2} , Chunwei Zhang _{1,

2} , Hao Kan _{1,

2} , Guozhen Shen ₃

Affiliation

With the boom of artificial intelligence (AI) and big data, electronics demand faster computing speed and lower power consumption, however, von Neumann architecture of current devices feature severe drawbacks for the further improvement of computing capability due to its design with separated memory and central processing unit (CPU). Fortunately, emerging nonvolatile memory devices, especially memristors, exhibit tremendous advantages in breaking the “memory wall” between memory and CPU by virtue of their in-computing and neuromorphic computing abilities. Here, a WO₃/HfO₂ heterojunction-based memristor is proposed, and the device exhibits extraordinary resistive switching behaviors (e.g., high ON/OFF ratio, stable endurance, long retention time, and multilevel resistance states) and neuromorphic characteristics (long-term/short-term synaptic activities). Further, the mechanism underlying the electrical performances of this device is studied. Silver conductive filaments and Schottky barrier models are proposed and explained successfully. Additionally, a multilayer layer perceptron neural network is constructed in terms of the memristor model, and variables embracing learning rate, algorithm, and training epochs, are explored to enhance the recognition accuracy of the network. Undoubtedly, the proposed high-quality WO₃/HfO₂ heterojunction-based memristor contributes to promoting the development of high-density storage and neuromorphic computing technology, showing fascinating prospects in the era of AI.

中文翻译：

用于高密度存储和神经形态计算的 HfO2/WO3 异质结结构忆阻器

随着人工智能（AI）和大数据的蓬勃发展，电子产品需要更快的计算速度和更低的功耗，然而，当前设备的冯·诺依曼架构由于其内存和中央分离的设计，对于进一步提高计算能力存在严重缺陷。处理单元（CPU）。幸运的是，新兴的非易失性存储器件，尤其是忆阻器，凭借其在计算和神经形态计算能力，在打破内存和 CPU 之间的“内存墙”方面展现出巨大优势。此处，WO ₃ /HfO ₂提出了基于异质结的忆阻器，该器件表现出非凡的电阻开关行为（例如，高开/关比、稳定的耐久性、长保留时间和多级电阻状态）和神经形态特征（长期/短期突触活动） . 此外，还研究了该器件电气性能的潜在机制。成功地提出并解释了银导电丝和肖特基势垒模型。此外，根据忆阻器模型构建了多层感知器神经网络，探索了包括学习率、算法和训练周期在内的变量，以提高网络的识别精度。毫无疑问，所提出的高质量 WO ₃ /HfO ₂基于异质结的忆阻器有助于推动高密度存储和神经形态计算技术的发展，在人工智能时代展现出迷人的前景。

更新日期：2022-10-17

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