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Perovskite-phase interfacial intercalated layer-induced performance enhancement in SrFeOx-based memristors

Chen Kai-Hui ,  Fan Zhen ,  Dong Shuai ,  Li Wen-Jie ,  Chen Yi-Hong ,  Tian Guo ,  Chen De-Yang ,  Qin Ming-Hui ,  Zeng Min ,  Lu Xu-Bing ,  Zhou Guo-Fu ,  Gao Xing-Sen ,  Liu Jun-Ming

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Perovskite-phase interfacial intercalated layer-induced performance enhancement in SrFeOx-based memristors

Chen Kai-Hui, Fan Zhen, Dong Shuai, Li Wen-Jie, Chen Yi-Hong, Tian Guo, Chen De-Yang, Qin Ming-Hui, Zeng Min, Lu Xu-Bing, Zhou Guo-Fu, Gao Xing-Sen, Liu Jun-Ming
Acta Phys. Sin., 2023, 72(9): 097301.
doi: 10.7498/aps.72.20221934
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  • Abstract

    SrFeOx (SFO) is a kind of material that can undergo a reversible topotactic phase transformation between an SrFeO2.5 brownmillerite (BM) phase and an SrFeO3 perovskite (PV) phase. This phase transformation can cause drastic changes in physical properties such as electrical conductivity, while maintaining the lattice framework. This makes SFO a stable and reliable resistive switching (RS) material, which has many applications in fields like RS memory, logic operation and neuromorphic computing. Currently, in most of SFO-based memristors, a single BM-SFO layer is used as an RS functional layer, and the working principle is the electric field-induced formation and rupture of PV-SFO conductive filaments (CFs) in the BM-SFO matrix. Such devices typically exhibit abrupt RS behavior, i.e. an abrupt switching between high resistance state and low resistance state. Therefore, the application of these devices is limited to the binary information storage. For the emerging applications like neuromorphic computing, the BM-SFO single-layer memristors still face problems such as a small number of resistance states, large resistance fluctuation, and high nonlinearity under pulse writing. To solve these problems, a BM-SFO/PV-SFO double-layer memristor is designed in this work, in which the PV-SFO layer is an oxygen-rich interfacial intercalated layer, which can provide a large number of oxygen ions during the formation of CFs and withdraw these oxygen ions during the rupture of CFs. This allows the geometric size (e.g., diameter) of the CFs to be adjusted in a wide range, which is beneficial to obtaining continuously tunable, multiple resistance states. The RS behavior of the designed double-layer memristor is studied experimentally. Compared with the single-layer memristor, it exhibits good RS repeatability, small resistance fluctuation, small and narrowly distributed switching voltages. In addition, the double-layer memristor exhibits stable and gradual RS behavior, and hence it is used to emulate synaptic behaviors such as long-term potentiation and depression. A fully connected neural network (ANN) based on the double-layer memristor is simulated, and a recognition accuracy of 86.3% is obtained after online training on the ORHD dataset. Comparing with a single-layer memristor-based ANN, the recognition accuracy of the double-layer memristor-based one is improved by 69.3%. This study provides a new approach to modulating the performance of SFO-based memristors and demonstrates their great potential as artificial synaptic devices to be used in neuromorphic computing.

    Authors and contacts

    • 1.

      Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China

    • 2.

      Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & National Center for International Research on Green Optoelectronics, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China

    • 3.

      Laboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China

      • Corresponding author: Fan Zhen, fanzhen@m.scnu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 92163210, U1932125, 52172143) and the Science and Technology Program of Guangzhou, China (Grant No. 202201000008).

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  • 图 1  (a) BM-SFO单层忆阻器、(b) BM-SFO/PV-SFO双层忆阻器结构示意图; (c) 单层忆阻器、(d)双层忆阻器的脉冲响应示意图, 其中LTP和LTD分别表示长时程增强和抑制; (e) BM-SFO/PV-SFO双层膜(红色)和BM-SFO单层膜(蓝色)的XRD θ-2θ扫描图

    Figure 1.  Schematic diagram of the structures of the (a) BM-SFO single-layer memristor and (b) BM-SFO/PV-SFO double-layer memristor; schematic curves showing the responses of the (c) single-layer memristor and (d) double-layer memristor to potentiation and depression pulses, i.e., the long-term potentiation and depression (LTP and LTD, respectively) characteristics; (e) XRD θ-2θ scans of BM-SFO/PV-SFO double-layer film (red) and BM-SFO single-layer film (blue)

    DownLoad: Full-Size Img PowerPoint

    图 2  (a) BM-SFO单层忆阻器和BM-SFO/PV-SFO双层忆阻器的I-V特性曲线对比; (b) 单层忆阻器和(c)双层忆阻器的I-V特性曲线(50次循环); (d)单层忆阻器和双层忆阻器的切换电压分布; (e)单层忆阻器和双层忆阻器的LRS和HRS电导分布

    Figure 2.  (a) Comparison of I-V characteristics of BM-SFO single-layer and BM-SFO/PV-SFO double-layer memristors; 50 cycles of I-V characteristics of (b) single-layer memristor and (c) double-layer memristor; (d) statistical distributions of switching voltages of single-layer and double-layer memristors; (e) statistical distributions of LRS and HRS conductances of single-layer and double-layer memristors.

    DownLoad: Full-Size Img PowerPoint

    图 3  BM-SFO/PV-SFO双层忆阻器导电细丝(a)连接和(b)断裂示意图; BM-SFO单层忆阻器导电细丝(c)连接和(d)断裂示意图

    Figure 3.  Schematic diagrams showing the conductive filament (a) connection and (b) rupture in the BM-SFO/PV-SFO double-layer memristor; schematic diagrams showing the conductive filament (c) connection and (d) rupture in the BM-SFO single-layer memristor.

    DownLoad: Full-Size Img PowerPoint

    图 4  (a) BM-SFO单层忆阻器和(b) BM-SFO/PV-SFO双层忆阻器在不同幅值正脉冲串作用下的电导变化; (c)单层忆阻器和(d)双层忆阻器在不同幅值负脉冲串作用下的电导变化

    Figure 4.  Conductance evolutions of (a) BM-SFO single-layer memristor and (b) BM-SFO/PV-SFO double-layer memristor under positive pulse trains with different amplitudes; conductance evolutions of (c) single-layer memristor and (d) double-layer memristor under negative pulse trains with different amplitudes.

    DownLoad: Full-Size Img PowerPoint

    图 5  (a) BM-SFO/PV-SFO双层忆阻器的LTP和LTD特性曲线, 所施加脉冲如插图所示; 双层忆阻器在(b) LTP和(c) LTD过程中电导态保持特性; (d)双层忆阻器多次循环下的LTP和LTD特性曲线, 上方插图展示了所施加脉冲

    Figure 5.  (a) LTP and LTD characteristics of the BM-SFO/PV-SFO double-layer memristor, and the insets show the schematics of applied pulses; retention of the conductance states of the double-layer memristor during the (b) LTP and (c) LTD processes; (d) multi-cycle LTP and LTD characteristics of the double-layer memristor, and the upper inset shows schematics of the applied pulses.

    DownLoad: Full-Size Img PowerPoint

    图 6  (a) 仿真构建的以SFO基忆阻器作为突触的全连接神经网络(ANN)结构示意图, W为连接输入层和输出层忆阻器突触的权重; (b) SFO基忆阻器交叉阵列示意图, 其中I为输入神经元, O为输出神经元; (c)以BM-SFO/PV-SFO双层忆阻器和BM-SFO单层忆阻器作为突触的ANN的准确率对比; (d)双层忆阻器基ANN的测试结果的混淆矩阵

    Figure 6.  (a) Schematic diagrams of the simulated fully connected neural network (ANN) using SFO-based memristors as synapses, W is the weight of the memristor synapse connecting the input layer and the output layer; (b) schematic diagram of the SFO-based memristor crossbar, where I is the input neuron and O is the output neuron; (c) comparison of accuracies of BM-SFO/PV-SFO double-layer memristor-based ANN and BM-SFO single-layer memristor-based ANN; (d) confusion matrix of the test results from the double-layer memristor-based ANN.

    DownLoad: Full-Size Img PowerPoint

    表 1  基于不同材料的导电细丝型忆阻器的主要器件性能

    Table 1.  Device performance of filament-type RS memories based on different materials.

    器件开关比Set电压/VReset电压/V阻态数文献
    TiN/Hf/HfOx/TiN>10+1.1+1.82[31]
    Pt/Ta2O5–x/TaO2–x/Pt>10–4.5+62[32, 33]
    Ag/NiOx/Pt>10±1.1±0.52[34, 35]
    SiO2/TiN/WOx/SiO2≈10+3+3.32—3[36, 37]
    Al/TiOx/ITO>102+2–22[38]
    Ag/ZnOx/Pt≈107+3–32[39]
    Pt/Ti/a-SrTiOx/Pt>102–1.35+1.92[40]
    Au/Cr/BaTiO3/Nb:SrTiO3/In>104–7–18[41]
    Au/BiFeO3/Pt>10+4–62[42]
    Au/SrFeO2.5/SrRuO3≈102–5+32[23]
    Ag/STO:Ag/SiO2/p++–Si≈102+3–360[43]
    Au/HfSe2/Ti≈102+1–1.226[44]
    Ag/Ti3C2Tx NS/Pt≈102+3+0.512[45]
    Au/SrFeO2.5/SrFeO3/SrRuO3≈102+0.7–1.432本工作
    DownLoad: CSV

    表 2  不同忆阻器作为突触的ANN图像识别准确率对比

    Table 2.  Image recognition accuracy comparison between ANNs using different memristors as synapses.

    器件阻态数ANN结构准确率/%数据集文献
    Pt/Li4Ti5O12/TiO2/Pt1003层网络(400×100×10)87MNIST(20×20)[46]
    Pt/TaOy/NP TaOx /Ta2003层网络(784×7840×10)89MNIST(28×28)[47]
    Ti/PdSe2/Au2003层网络(400×100×10)94MNIST(20×20)[48]
    Ta/HfO2/Pt2003层网络(64×54×10)91MNIST (8×8, 由20×20 下采样获得)[49]
    Ag/WSe2 QDs/ La0.3Sr0.7MnO3/SrTiO3703层网络 (NA)91ORHD(8×8)[50]
    Au/SrFeO2.5/SrFeO3/SrRuOx322层网络 (64×10)86ORHD(8×8)本工作
    DownLoad: CSV
    Baidu
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    [2]

    Vaccaro F, Brivio S, Perotto S, Mauri A G, Spiga S 2022 Neuromorph. Comput. Eng. 2 021003Google Scholar

    [3]

    Li C, Li W, Wang F, Zhang J, Sun J, Shen J, Hu K, Zhao J, Zhang K 2020 Mater. Sci. Semicond. Process. 116 105103Google Scholar

    [4]

    Clima S, Chen Y Y, Fantini A, Goux L, Degraeve R, Govoreanu B, Pourtois G, Jurczak M 2015 IEEE Electron Device Lett. 36 769Google Scholar

    [5]

    Jiang Y, Zhang K, Hu K, Zhang Y, Liang A, Song Z, Song S, Wang F 2021 Mater. Sci. Semicond. Process. 136 106131Google Scholar

    [6]

    Ban S, Kim O 2014 Jpn. J. Appl. Phys. 53 06JE15Google Scholar

    [7]

    Lubben M, Karakolis P, Vassilios I, Mormand P, Dimitrakis P, Valov I 2015 Adv. Mater. 28 6202Google Scholar

    [8]

    Stecconi T, Guido R, Berchialla L, Porta A L, Weiss J, Popoff Y, Halter M, Sousa M, Horst F, Davila D, Drechsler U, Dittmann R, Offrein B J, Bragaglia V 2022 Adv. Electron. Mater. 8 220048Google Scholar

    [9]

    Marinella M J, Dalton S M, Mickel P R, Dodd P E, Shaneyfelt M R, Bielejec E, Vizkelethy G, Kotula P G 2012 IEEE Trans. Nucl. Sci. 59 2987Google Scholar

    [10]

    Hur J H, Lee M J, Lee C B, Kim Y B, Kim C J 2010 Phys. Rev. B 82 155321Google Scholar

    [11]

    Hughart D R, Lohn A J, Mickel P R, Dalton S M, Dodd P E, Shaneyfelt M R, Silva A I, Bielejec E, Vizkelethy G, Marshall M T, Mclain M L, Marinella M J 2013 IEEE Trans. Nucl. Sci. 60 4512Google Scholar

    [12]

    Palagushkin A N, Roshchupkin D V, Yudkin F A, Irzhak D V, Keplinger O, Privezentsev V V 2018 J. Appl. Phys. 124 205109Google Scholar

    [13]

    Kim T H, Kim M H, Bang S, Lee D K, Kim S, Cho S, Park B G 2020 IEEE Trans. Nanotechnol. 19 475Google Scholar

    [14]

    Kim M, Yoo K, Jeon S P, Park S K, Kim Y H 2020 Micromachines 11 154Google Scholar

    [15]

    Jang J, Gi S, Yeo I, Choi S, Jang S, Ham S, Lee B, Wang G 2022 Adv. Sci. 9 2201117Google Scholar

    [16]

    Zhou G, Sun B, Hu X, Sun L, Zou Z, Xiao B, Qiu W, Wu B, Li J, Han J, Liao L, Xu C, Xiao G, Xiao L, Cheng J, Zheng S, Wang L, Song Q, Duan S 2021 Adv. Sci. 8 2003765Google Scholar

    [17]

    Liu J, Yang H, Ma Z, Chen K, Huang X, Wang K 2020 J. Appl. Phys. 128 184902Google Scholar

    [18]

    Kwon D H, Kim K M, Jang J H, Jeon J M, Lee M H, Kim G H, Li X S, Park G S, Lee B, Han S, Kim M, Hwang C S 2010 Nat. Nanotechnol. 5 148Google Scholar

    [19]

    Khare A, Shin D, Yoo T S, Kim M, Kang T D, Lee J, Roh S, Jung I H, Hwang J, Kim S W, Noh T W, Ohta H, Choi W S 2017 Adv. Mater. 29 1606566Google Scholar

    [20]

    Saleem M S, Cui B, Song C, Sun Y, Gu Y, Zhang R, Fayaz M U, Zhou X, Werner P, Parkin S S P, Pan F 2019 ACS Appl. Mater. Interfaces 11 6581Google Scholar

    [21]

    Nallagatla V R, Jung C U 2020 Appl. Phys. Lett. 117 143503Google Scholar

    [22]

    Acharya S K, Nallagatla R V, Togibasa O, Lee B W, Liu C, Jung C U, Park B H, Park J Y, Cho Y, Kim D W, Jo J, Kwon D H, Kim M, Hwang C S, Chae S C 2016 ACS Appl. Mater. Interfaces 8 7902Google Scholar

    [23]

    Tian J, Wu H, Fan Z, Zhang Y, Pennycook S J, Zheng D, Tan Z, Guo H, Yu P, Lu X, Zhou G, Gao X, Liu J M 2019 Adv. Mater. 31 1903679Google Scholar

    [24]

    Acharya S K, Jo J, Raveendra N V, Dash U, Kim M, Baik H, Lee S, Park B H, Lee J S, Chae S C, Hwang C S, Jung C U 2017 Nanoscale 9 10502Google Scholar

    [25]

    Su R, Chen J, Zhao Y, Cheng W, Li Y, Wang X, Yan J, Miao X 2022 Ceram. Int. 48 33115Google Scholar

    [26]

    Lyapunov N, Suen C H, Wong C M, Tang X, Ho Z L, Zhou K, Chen X X, Liu H M, Zhou X, Dai J Y 2021 J. Adv. Dielectr. 11 2150004Google Scholar

    [27]

    Rao J, Fan Z, Hong L, Cheng S, Huang Q, Zhao J, Xiang X, Guo E J, Guo H, Hou Z, Chen Y, Lu X, Zhou G, Gao X, Liu J M 2021 Mater. Today Phys. 18 110392Google Scholar

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  • Received Date:  10 October 2022
  • Accepted Date:  02 December 2022
  • Available Online:  28 December 2022
  • Published Online:  05 May 2023

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