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SrFeOx (SFO)是一种能在SrFeO2.5钙铁石(BM)相和SrFeO3钙钛矿(PV)相之间发生可逆拓扑相变的材料. 这种相变能显著改变电导却维持晶格框架不变, 使SFO成为一种可靠的阻变材料. 目前大部分SFO基忆阻器使用单层BM-SFO作为阻变功能层, 这种器件一般表现出突变型阻变行为, 因而其应用被局限于两态存储. 对于神经形态计算等应用, 单层BM-SFO忆阻器存在阻态数少、阻值波动大等问题. 为解决这些问题, 本研究设计出BM-SFO/PV-SFO双层忆阻器, 其中PV-SFO层为富氧界面插层, 可在导电细丝形成过程中提供大量氧离子并在断裂过程中回收氧离子, 使导电细丝的几何尺寸(如直径)在更大范围内可调, 从而获得更多、更连续且稳定的阻态, 可用于模拟长时程增强和抑制等突触行为. 基于该器件仿真构建了全连接神经网络(ANN), 在手写体数字光学识别(ORHD)数据集进行在线训练后获得了86.3%的识别准确率, 相比于单层忆阻器基ANN的准确率提升69.3%. 本研究为SFO基忆阻器性能调控提供了一种新方法, 并展示了它们作为人工突触器件在神经形态计算方面的应用潜力.
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关键词:
- SrFeOx基忆阻器 /
- 人工突触 /
- 神经网络 /
- 界面插层
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.-
Keywords:
- SrFeOx-based memristor /
- artificial synapse /
- neural network /
- interfacial intercalated layer
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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
Fig. 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)
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
Fig. 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.
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
Fig. 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.
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
Fig. 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.
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
Fig. 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.
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
Fig. 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.
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/V Reset电压/V 阻态数 文献 TiN/Hf/HfOx/TiN >10 +1.1 +1.8 2 [31] Pt/Ta2O5–x/TaO2–x/Pt >10 –4.5 +6 2 [32, 33] Ag/NiOx/Pt >10 ±1.1 ±0.5 2 [34, 35] SiO2/TiN/WOx/SiO2 ≈10 +3 +3.3 2—3 [36, 37] Al/TiOx/ITO >102 +2 –2 2 [38] Ag/ZnOx/Pt ≈107 +3 –3 2 [39] Pt/Ti/a-SrTiOx/Pt >102 –1.35 +1.9 2 [40] Au/Cr/BaTiO3/Nb:SrTiO3/In >104 –7 –1 8 [41] Au/BiFeO3/Pt >10 +4 –6 2 [42] Au/SrFeO2.5/SrRuO3 ≈102 –5 +3 2 [23] Ag/STO:Ag/SiO2/p++–Si ≈102 +3 –3 60 [43] Au/HfSe2/Ti ≈102 +1 –1.2 26 [44] Ag/Ti3C2Tx NS/Pt ≈102 +3 +0.5 12 [45] Au/SrFeO2.5/SrFeO3/SrRuO3 ≈102 +0.7 –1.4 32 本工作 
下载: 导出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/Pt 100 3层网络(400×100×10) 87 MNIST(20×20) [46] Pt/TaOy/NP TaOx /Ta 200 3层网络(784×7840×10) 89 MNIST(28×28) [47] Ti/PdSe2/Au 200 3层网络(400×100×10) 94 MNIST(20×20) [48] Ta/HfO2/Pt 200 3层网络(64×54×10) 91 MNIST (8×8, 由20×20 下采样获得) [49] Ag/WSe2 QDs/ La0.3Sr0.7MnO3/SrTiO3 70 3层网络 (NA) 91 ORHD(8×8) [50] Au/SrFeO2.5/SrFeO3/SrRuOx 32 2层网络 (64×10) 86 ORHD(8×8) 本工作
下载: 导出CSV
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