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Article type: Research Article
Authors: Zhou, Qiaozhen | Wang, Fang | Zhao, Xuanyu | Hu, Kai | Zhang, Yujian | Shan, Xin | Lin, Xin | Zhang, Yupeng | Shan, Ke | Zhang, Kailiang; *
Affiliations: Tianjin Key Laboratory of Film Electronic andCommunication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, China
Correspondence: [*] Corresponding author. Kailiang Zhang, Tianjin Key Laboratory of Film Electronic and Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, China. E-mail: kailiang_zhang2007@163.com.
Abstract: Resistive random access memory (RRAM) has lots of advantages that make it a promising candidate for ultra-high-density memory applications and neuromorphic computing. However, challenges such as high forming voltage, low endurance, and poor uniformity have hampered the development and application of RRAM. To improve the uniformity of the resistive memory, this paper systematically investigates the HfOx-based RRAM by embedding nanoparticles. In this paper, the HfOx-Based RRAM with and without tungsten nanoparticles (W NPs) is fabricated by magnetron sputtering, UV lithography, and stripping. Comparing the various resistive switching behaviors of the two devices, it can be observed that the W NPs device exhibits lower switching voltage (including a 69.87% reduction in Vforming and a reduction in Vset/Vreset from 1.4 V/-1.36 to 0.7 V/-1.0 V), more stable cycling endurance (>105 cycles), and higher uniformity. A potential switching mechanism is considered based on the XPS analysis and the research on the fitting of HRS and LRS: Embedding W NPs can improve the device performance by inducing and controlling the conductive filaments (CFs) size and paths. This thesis has implications for the performance enhancement and development of resistive memory.
Keywords: Resistive random access memory (RRAM), HfOx, embedding W nanoparticles, uniformity, conduction mechanism
DOI: 10.3233/JIFS-232028
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5159-5167, 2023
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