神经形态工程学
材料科学
锡
导电体
光电子学
纳米结构
图层(电子)
随机存取
电阻随机存取存储器
沉积(地质)
电极
非易失性存储器
纳米技术
计算机科学
电气工程
复合材料
电压
人工神经网络
工程类
物理化学
古生物学
机器学习
化学
操作系统
冶金
生物
沉积物
作者
Ying‐Chun Shen,Yuesheng Huang,Tzu‐Yi Yang,Yi‐Jen Yu,Hao‐Chung Kuo,Tseung–Yuen Tseng,Yu‐Lun Chueh
标识
DOI:10.1021/acsanm.3c00810
摘要
Conductive bridge random access memory (CBRAM) is one of the promising nonvolatile memories for next-generation technology owing to its high density, low power consumption, and fast switching speed, which is also a potential candidate for implementation of neuromorphic computing. However, CBRAMs suffer from stochastically growing conducting filaments in the insulator layer. Herein, we demonstrated Al2O3 sandglass nanostructures (SNGSs) embedded into HfOx-based CBRAMs via glancing angle deposition technology with the AlN thermal enhanced layer to prevent the overinjection of cations and localize the growth of conducting filaments in the HfOx switching layer. With the assistance of Al2O3 SNGSs and the AlN layer, the Cu/Al2O3 SNGSs/HfOx/AlN/TiN device exhibited a stable on/off ratio of >10 for more than 6000 cycles. Furthermore, with a Te top electrode, the Te/Al2O3 SNGSs/HfOx/AlN/TiN device shows a multilevel cell characteristic by controlling compliance currents. In addition, it possesses excellent potentiation and depression nonlinearities of 1.28 and 0.4, respectively, which is beneficial for future applications in neuromorphic computing.
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