基于单壁碳纳米管导线电极的ReRAM开关性能

Dong-Yul Jang, Beomso Jo, Y. L. Kim, M. Kwon
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摘要

从过去开始,内存/计算功能瓶颈的出现一直是冯·诺依曼架构的一个问题。随着计算和数据存储需求的增加,开发高性能存储器以克服运算速度和低功耗的限制是必要的。最近,神经形态系统随着模仿人类大脑的计算技术而成为人们关注的焦点。神经形态结构是实现基于脉冲神经网络(SNN)的系统硬件与人类突触学习的重要因素。在神经形态记忆系统的候选者中,电阻式随机存取存储器(ReRAM)以其简单的结构和易于设计的硅基CMOS技术而备受关注。根据突触的学习规则(spike timing dependent plasticity, STDP),突触权重的变化等于突触ReRAM器件电阻的变化。因此,本研究通过合成单壁碳纳米管(SWCNTs),提出了一种基于纳米线电极的具有金属-碳纳米管-氧化硅(MCOS)结构的非易失性突触器件。SWCNTs具有优异的电性能,其导电性比铜高,载流子迁移率比Si高。由于顶电极丝作为SWCNTs,减少了漏电流,并通过每个电池的自集成显示出高开关性能。最终,它将成为器件实现的重要参数,提高存储器的可靠性和电导灯丝(CF)的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ReRAM Switching Performance based on Single-Walled Carbon Nanotubes Wire Electrode
From the past, the occurrence of bottlenecks in memory/computation functions has been a problem for Von Neumann architecture. As the demand for computing and data storage increases, it is necessary to develop high-performance memory to overcome the limitations of operating speed and low power consumption. Recently, neuromorphic systems have been in the spotlight with computing technology that imitates the human brain. Neuromorphic architecture can be an important factor in implementing spike neural network (SNN) – based system hardware with human synaptic learning. Among the candidates for neuromorphic memory systems, resistive random-access memory (ReRAM) is attracting attention for its simple structure and easy design with silicon-based CMOS technology. By spike timing dependent plasticity (STDP), known as the learning rule of synapses, the change in the weight of synapses is equal to the change in the resistance of the synaptic ReRAM device.Therefore, this study proposed a nano-wire electrode based non-volatile synaptic devices with metal-CNTs-oxide-Si (MCOS) structure by synthesizing single-walled carbon nanotubes (SWCNTs). SWCNTs have excellent electrical properties as they include higher conductivity than copper and have carrier mobility than Si. As the top electrode wire was applied as SWCNTs, the leakage current was reduced, and the high switching performance was shown through self-integration of each cell. Ultimately, it will be an important parameter of device implementation that improves the reliability of memory and the stability of conductance filament (CF).
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