电铸
神经形态工程学
记忆电阻器
材料科学
光电子学
蛋白质丝
电阻随机存取存储器
堆栈(抽象数据类型)
重置(财务)
非易失性存储器
切换时间
图层(电子)
氧化物
纳米技术
哈夫尼亚
电子工程
计算机科学
电气工程
电压
复合材料
人工神经网络
工程类
经济
机器学习
冶金
金融经济学
程序设计语言
作者
Na Bai,Baoyi Tian,Ge‐Qi Mao,Kan‐Hao Xue,Tao Wang,Jun‐Hui Yuan,Xiaoxin Liu,Zhaonan Li,Shen Guo,Zuopai Zhou,Nian Liu,Hong Lu,Xiaodong Tang,Huajun Sun,Xiangshui Miao
摘要
The fast development of high-accuracy neuromorphic computing requires stable analog memristors. While filamentary memory switching is very common in binary oxides, their resistive switching usually involves abrupt changes due to the rupture or reformation of metallic filaments. In this work, we designed a memristor consisting of dual-layer HfOy/HfOx, with different concentrations of oxygen vacancies (y > x). During the electroforming process, both the migration of existing oxygen vacancies in HfOx and the generation of new oxygen vacancies in HfOy occur simultaneously, leaving a semiconducting part close to the HfOy/HfOx interface. The resulting filament is not metallic as a whole, as revealed by first principles calculations. Such a device demonstrates excellent switching uniformity as well as highly gradual resistance change, ideal for neuromorphic computing. Through fine tuning of the filament structure, the device achieves low variation, high speed, gradual SET and RESET processes, and hundreds of stable multi-level state behaviors. The long-term synaptic plasticity was further achieved, showing good linearity and large analog switching window (ΔG as high as 487.5 μS). This works affords a route toward a gradual resistance change in oxide-based memristors.
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