Dou Hongyi, Lin Zehao, Hu Zedong, Tsai Benson Kunhung, Zheng Dongqi, Song Jiawei, Lu Juanjuan, Zhang Xinghang, Jia Quanxi, MacManus-Driscoll Judith L, Ye Peide D, Wang Haiyan
School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States.
Elmore School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907, United States.
Nano Lett. 2023 Nov 8;23(21):9711-9718. doi: 10.1021/acs.nanolett.3c02217. Epub 2023 Oct 24.
Filamentary-type resistive switching devices, such as conductive bridge random-access memory and valence change memory, have diverse applications in memory and neuromorphic computing. However, the randomness in filament formation poses challenges to device reliability and uniformity. To overcome this issue, various defect engineering methods have been explored, including doping, metal nanoparticle embedding, and extended defect utilization. In this study, we present a simple and effective approach using self-assembled uniform Au nanoelectrodes to controll filament formation in HfO resistive switching devices. By concentrating the electric field near the Au nanoelectrodes within the BaTiO matrix, we significantly enhanced the device stability and reduced the threshold voltage by up to 45% in HfO-based artificial neurons compared to the control devices. The threshold voltage reduction is attributed to the uniformly distributed Au nanoelectrodes in the insulating matrix, as confirmed by COMSOL simulation. Our findings highlight the potential of nanostructure design for precise control of filamentary-type resistive switching devices.
丝状电阻开关器件，如导电桥随机存取存储器和价态变化存储器，在存储器和神经形态计算中有多种应用。然而，丝状结构形成的随机性对器件的可靠性和均匀性提出了挑战。为克服这一问题，人们探索了各种缺陷工程方法，包括掺杂、金属纳米颗粒嵌入和扩展缺陷利用。在本研究中，我们提出了一种简单有效的方法，即使用自组装均匀金纳米电极来控制HfO电阻开关器件中的丝状结构形成。通过将电场集中在BaTiO基质内的金纳米电极附近，与对照器件相比，我们显著提高了基于HfO的人工神经元的器件稳定性，并将阈值电压降低了高达45%。阈值电压的降低归因于绝缘基质中均匀分布的金纳米电极，这一点通过COMSOL模拟得到了证实。我们的研究结果突出了纳米结构设计在精确控制丝状电阻开关器件方面的潜力。
