Tuning Ionic Transport in Memristive Devices by Graphene with Engineered Nanopores

石墨烯 材料科学 纳米孔 纳米技术 离子键合 记忆电阻器 神经形态工程学 氧化物 离子 计算机科学 化学 电子工程 工程类 机器学习 人工神经网络 有机化学 冶金
作者
Jihang Lee,Chao Du,Kai Sun,Emmanouil Kioupakis,Wei Lü
出处
期刊:ACS Nano [American Chemical Society]
卷期号:10 (3): 3571-3579 被引量:175
标识
DOI:10.1021/acsnano.5b07943
摘要

Memristors, based on inherent memory effects in simple two-terminal structures, have attracted tremendous interest recently for applications ranging from nonvolatile data storage to neuromorphic computing based on non-von Neumann architectures. In a memristor, the ability to modulate and retain the state of an internal variable leads to experimentally observed resistive switching (RS) effects. Such phenomena originate from internal, microscopic ionic migration and associated electrochemical processes that modify the materials' electrical and other physical properties. To optimize the device performance for practical applications with large-size arrays, controlling the internal ionic transport and redox reaction processes thus becomes a necessity, ideally at the atomic scale. Here we show that the RS characteristics in tantalum-oxide-based memristors can be systematically tuned by inserting a graphene film with engineered nanopores. Graphene, with its atomic thickness and excellent impermeability and chemical stability, can be effectively integrated into the device stack and can offer unprecedented capabilities for the control of ionic dynamics at the nanoscale. In this device structure, the graphene film effectively blocks ionic transport and redox reactions; thereby the oxygen vacancies required during the RS process are allowed to transport only through the engineered nanosized openings in the graphene layer, leading to effective modulation of the device performance by controlling the nanopore size in graphene. The roles of graphene as an ion-blocking layer in the device structure were further supported by transmission electron microscopy, energy-dispersive X-ray spectroscopy, and atomistic simulations based on first-principles calculations.
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