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材料导报  2025, Vol. 39 Issue (11): 25020107-6    https://doi.org/10.11896/cldb.25020107
  无机非金属及其复合材料 |
高稳定性氧化镓基忆阻器的构筑和神经形态计算应用
王淼儒, 柴晓杰*, 闫泽宇, 索丁丁, 冀健龙*
太原理工大学集成电路学院,山西 晋中 030600
Development and Construction of High-stability Gallium Oxide-based Memristors for Neuromorphic Computing Applications
WANG Miaoru, CHAI Xiaojie*, YAN Zeyu, SUO Dingding, JI Jianlong*
School of Integrated Circuit, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
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摘要 在后摩尔时代,忆阻器因其能够模拟生物突触功能,可被用于开发高性能的类脑计算。然而,如何实现兼具鲁棒性与低功耗的人工突触器件成为当前的研究重点。选用氟掺杂的氧化锡(FTO)作为衬底,采用磁控溅射技术制备非晶的GaOx作为阻变层,构建了Ag/GaOx/FTO忆阻器。研究表明,该器件表现出优异的多级存储特性,包括保持时间将近104 s、循环次数超过150次以及超过100个状态的高线性电导调制。进一步模拟了经典的“巴甫洛夫狗”条件反射过程,并基于Ag/GaOx/FTO忆阻器阵列搭建了卷积神经网络(CNN),实现了60 000张手写数字图像的有效识别,准确率达到91.34%。本工作表明,Ag/GaOx/FTO忆阻器在神经形态计算领域中具有巨大的潜力。
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王淼儒
柴晓杰
闫泽宇
索丁丁
冀健龙
关键词:  忆阻器  GaOx  神经突触  卷积神经网络    
Abstract: Memristors, promising nanoelectronic devices with the ability to emulate biological synaptic functionalities, have great potential for use in high-performance neuromorphic computing in the post-Moore era. However, realization of high-performance artificial synaptic devices with robustness and low-power consumption remains faces great challenges. Here, we report a memristor based on amorphous gallium oxide (GaOx) memristive layer structure, deposited via magnetron sputtering on fluorine-doped tin oxide (FTO) substrates. The device exhibits excellent multilevel storage properties, including retention times approaching 104 s, cycling endurance exceeding 150 cycles, and highly linear conductance modulation with over 100 distinct conductance states. Furthermore, classical “Pavlov’s dog” conditioned reflex behavior was successfully emulated. In addition, a convolutional neural network (CNN) based on an Ag/GaOx/FTO memristor array was constructed, effectively recognizing 60 000 handwritten digit images with an accuracy of 91.34%. Our findings underscore the significant potential of the developed Ag/GaOx/FTO memristor for applications in neuromorphic computing.
Key words:  memristor    GaOx    neural synapse    convolutional neural network
发布日期:  2025-05-29
ZTFLH:  TN60  
基金资助: 国家自然科学基金(52203316);中国博士后科学基金(2024M762331)
通讯作者:  *柴晓杰,博士,太原理工大学集成电路学院讲师、硕士研究生导师。目前主要从事神经形态忆阻器、新型传感器等方面的研究。chaixiaojie@tyut.edu.cn
冀健龙,博士,太原理工大学集成电路学院副院长/教授、博士研究生导师。目前主要从事神经形态芯片、微纳传感器、智能微系统等方面的研究。jijianlong@tyut.edu.cn   
作者简介:  王淼儒,太原理工大学集成电路学院硕士研究生,在柴晓杰老师的指导下进行研究。目前主要研究领域为神经形态忆阻器。
引用本文:    
王淼儒, 柴晓杰, 闫泽宇, 索丁丁, 冀健龙. 高稳定性氧化镓基忆阻器的构筑和神经形态计算应用[J]. 材料导报, 2025, 39(11): 25020107-6.
WANG Miaoru, CHAI Xiaojie, YAN Zeyu, SUO Dingding, JI Jianlong. Development and Construction of High-stability Gallium Oxide-based Memristors for Neuromorphic Computing Applications. Materials Reports, 2025, 39(11): 25020107-6.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.25020107  或          https://www.mater-rep.com/CN/Y2025/V39/I11/25020107
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