Flexible TiO2-WO3−x hybrid memristor with enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

IF 12.3 1区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Jianyong Pan, Hao Kan, Zhaorui Liu, Song Gao, Enxiu Wu, Yang Li, Chunwei Zhang
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Abstract

Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.

Abstract Image

Abstract Image

具有增强线性和突触可塑性的柔性 TiO2-WO3-x 混合记忆晶体管,可用于神经形态计算中的精确权重调整
基于氧化钨(WO3)的忆阻器在神经形态计算中的应用前景广阔。然而,单层 WO3 记忆晶体管存在记忆性能弱和非线性电导变化等问题。本研究提出了一种基于 WO3-x 和 TiO2 混合体的功能层,用于构建具有出色突触特性的柔性忆阻器。对该忆阻器施加不同的电刺激可实现一系列突触功能,并通过导电丝模型阐明了其传导机制。二氧化钛的加入不仅增强了忆阻器的记忆特性,还使其在长期变化过程中的传导更加线性、对称和均匀。此外,鉴于掺杂 TiO2 增强了器件性能,该器件在简单行为模拟和复杂计算问题处理方面的潜力也得到了探索。该器件的 "学习-遗忘-再学习 "特性和可集成性得到了直观的展示。将该器件应用于卷积神经网络,MNIST 手写数字的识别准确率达到 98.7%。
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来源期刊
CiteScore
17.10
自引率
4.80%
发文量
91
审稿时长
6 weeks
期刊介绍: npj Flexible Electronics is an online-only and open access journal, which publishes high-quality papers related to flexible electronic systems, including plastic electronics and emerging materials, new device design and fabrication technologies, and applications.
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