论文类型：期刊论文
第一作者：袁俊辉,黄晓弟,李灏阳
通讯作者：缪向水
合写作者：薛堪豪,何毓辉,童浩,李祎,万天晴,卢一帆
发表刊物：Advanced Electronic Materials
所属单位：华中科技大学
学科门类：工学
一级学科：电子科学与技术
文献类型：J
卷号：6
期号：8
页面范围：2000309
关键字：conductive filaments memory switching memristors neuromorphic computing threshold switching
DOI码：10.1002/aelm.202000309
发表时间：4402-07-01
摘要：The fully memristive neural network is emerging as a game-changer in the artificial intelligence competition. Artificial synapses and neurons, as two fundamental elements for hardware neural networks, have been substantially implemented by different devices with memory and threshold switching (TS) behaviors, respectively. However, obtaining controllable memory and TS behaviors in the same memristive material system is still a considerable challenge that holds great potential for realizing compatible artificial neurons and synapses. Here, a heterogeneous bilayer conductive filamentary memristor comprising two different electrolytes with distinct copper ion mobility is reported: Cu/GeTe/Al2O3/Pt, which can demonstrate the governance of switching types. Experimentally, when the thickness of the Al2O3 layer is 3 nm, stable nonvolatile multilevel memory switching (MS) is observed and employed to mimic the synaptic plasticity. With increasing oxide thickness, the switching behavior under the same compliance current alters from MS to volatile TS and is used to emulate the integrate-and-fire neuron function. The controllable switching stems from the change in the metal filament morphology within the Al2O3 layer, which is supported by ab initio calculation results. This method enables a new pathway for constructing functionally reconfigurable neuromorphic devices for intelligence neuromorphic systems.
发布期刊链接：https://onlinelibrary.wiley.com/doi/abs/10.1002/aelm.202000309