基于接口的二阶记忆晶体管的通用突触可塑性

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Advanced Electronic Materials Pub Date : 2024-04-27 DOI:10.1002/aelm.202300803
Anton Khanas, Christian Hebert, David Hrabovsky, Loïc Becerra, Nathalie Jedrecy
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引用次数: 0

摘要

二阶忆阻器的内部短期动态与生物神经元的行为相似,是硬件神经形态网络的理想基础单元,旨在处理时空任务。这里研究的是 La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 二阶忆阻器,其电阻和温度依赖性在同一芯片上从半导体到金属不等,但表现出普遍的神经形态可塑性。所有器件都可以用一个紧凑的电流传导现象学模型来描述,表明电阻开关源自界面,通过电荷捕获实现。值得注意的是,无论装置类型如何,短期记忆增减和长期记忆巩固/遗忘的过程都是相同的。与大脑中的突触/神经元一样,只有突触传递权重和相对于刺激的兴奋/松弛时间不同。只需使用刺激频率(活动率)就能调整权重,而权重的变化则取决于先前的活动(历史)。金属和半导体器件显示出相同的激活或抑制动态,从一种状态过渡到另一种状态取决于历史。半导体器件的阈值频率略低。这项工作有助于更好地理解记忆开关和可塑性,对开发具有新编程范式的仿脑神经网络具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Universal Synaptic Plasticity of Interface-Based Second-Order Memristors

Second-order memristors with their internal short-term dynamics display behavioral similarities with biological neurons and constitute an ideal basis unit for hardware neuromorphic networks, aims at treating spatio-temporal tasks. Here, La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 second-order memristive devices are investigated whose resistances and temperature dependencies range, on the same chip, from semiconductor to metal, but exhibit a universal neuromorphic plasticity. All devices may be described using a compact phenomenological model of current conduction, showing that resistive switching originates from interfaces, through charge trapping. Remarkably, the processes of short-term memory gain/loss and long-term consolidation/forgetting are the same whatever the device type. Only the synaptic transmission weights and the excitation/relaxation times with respect to stimuli differ, as it occurs for synapses/neurons in the brain. The weights may be tuned by the sole use of the frequency of stimuli (the activity rate), their evolution being dependent on previous activities (the history). Metal and semiconductor devices display the same in-operando dynamics of potentiation or of depression, the transition from one regime to another being history-dependent. The threshold frequencies are slightly lower in semiconducting devices. This work contributes to better understanding of memristive switching and plasticity and is relevant for the development of brain-mimetic neural networks with new programming paradigms.

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来源期刊
Advanced Electronic Materials
Advanced Electronic Materials NANOSCIENCE & NANOTECHNOLOGYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
11.00
自引率
3.20%
发文量
433
期刊介绍: Advanced Electronic Materials is an interdisciplinary forum for peer-reviewed, high-quality, high-impact research in the fields of materials science, physics, and engineering of electronic and magnetic materials. It includes research on physics and physical properties of electronic and magnetic materials, spintronics, electronics, device physics and engineering, micro- and nano-electromechanical systems, and organic electronics, in addition to fundamental research.
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