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Memristor-like Electrical Resistivity Behavior of SiO2 Nanofilms and Their Applicability in Wireless Internet of Things Communications

记忆电阻器 材料科学 神经形态工程学 纳米技术 光电子学 电阻随机存取存储器 纳米材料 电压 电子工程 电气工程 计算机科学 人工神经网络 机器学习 工程类
作者
Takeru Suzuki,Katsuyoshi Ando,Takahisa Ichinohe,Seiichi Sato
出处
期刊:Meeting abstracts 卷期号:MA2024-02 (67): 4650-4650
标识
DOI:10.1149/ma2024-02674650mtgabs
摘要

Memristors, capable of altering their resistance based on past voltage inputs, have emerged as promising components for various applications, including resistive memory, neuromorphic devices, and chaotic circuits. Over the years, a variety of nanomaterials and nanodevices, such as nanocrystal arrays [1], metal-doped dielectrics [2,3], and ferroelectric switching devices [4], have been reported to exhibit memristive properties. In this study, we demonstrated memristor-like behavior in metal-contacted SiO 2 nanofilms. Unlike conventional methods used for inducing memristive behavior in oxide films, such as adjusting the density distribution of mobile ions or creating/disrupting conductive filaments within the oxide, we leveraged the unique electrical conductivity of the nanofilms to achieve memristor-like current–voltage (I–V) characteristics. This conductivity likely results from the many-body effects arising from the electron-electron and/or electron-hole interactions between metals and SiO 2 nanofilms [5]. A notable deviation from conventional memristors was observed when the resistance shifted from low to high resistance states as the voltage returned to 0 V. Despite this behavior, the SiO 2 nanofilms exhibited a pinched hysteresis loop in their I–V characteristics, and were thus suitable for application in chaotic circuits. The use of SiO 2 nanofilms offers advantages such as facile integration into Si microfabrication processes and realization of memristor-like properties through the intrinsic characteristics of SiO 2 , eliminating the need for precise doping processes. Herein, we investigated the alternating current characteristics of SiO 2 nanofilms in contact with Au films and Ag-pastes. The SiO 2 nanofilms were thermally grown on low-resistance Si substrates, followed by the deposition of metal electrodes. The deposited metals served not only as electrodes for injecting carriers into the nanofilms but also as inducers of many-body effects. SiO 2 nanofilms with thicknesses of ~1 nm, which facilitated easy carrier tunneling, exhibited resistance-specific behaviors in their I–V characteristics. Conversely, films with thicknesses exceeding 50 nm, which prevented carrier injection into SiO 2 , showed capacitance-specific properties. Memristor-like behavior was achieved by adjusting the SiO 2 film thicknesses to their intermediate values. For example, a 9-nm-thick SiO 2 nanofilm demonstrated a resistance change from over 1 kΩ·cm during voltage increase to below 10 Ω·cm during voltage decrease. This phenomenon is attributed to the SiO 2 nanofilms acting as wide-bandgap semiconductors upon metal contact, as well as in the presence of Schottky barriers and/or Fowler–Nordheim tunneling at the metal/SiO 2 interfaces. Based on our experimental results, we propose utilization of SiO 2 nanofilms in chaotic circuits for secure internet of things (IoT) device communication. Simulations were conducted using LTspice and MATLAB Simulink to encrypt digital signals by modulating chaotic voltage oscillation rates with the digital signals. The modulated voltage wave patterns were transmitted with white noise added to mimic real-world scenarios. At the receiving end, decryption was achieved using a similar SiO 2 nanofilm-integrated chaotic circuit, representing a form of hardware based symmetric key encryption. Simulations showed that the image data encryption/decryption performance degraded only slightly with increasing noise levels, even at signal-to-noise ratios of approximately 10 dB. Thus, the proposed device exhibits secure communication capabilities comparable to those of conventional memristor–incorporated circuits [6]. References [1] F. Wang, M. Yu, X. Chen, J. Li, Z. Zhang, Y. Li, G. Zhang, K. Shi, L. Shi, M. Zhang, T. Lu, J. Zhang, Smart Mater . 4 , e1135 [13 pages] (2023). [2] W. Tong, W. Wei, X. Zhang, S. Ding, Z. Lu, L. Liu, W. Li, C. Pan, L. Kong, Y. Wang, M. Zhu, S. Liang, F. Miao, Y. Liu, Nano Lett. 23 , 9928–9935 (2023). [3] X. Yan, Y. Shao, Z. Fang, X. Han, Z. Zhang, J. Niu, J. Sun, Y. Zhang, L. Wang, X. Jia, Z. Zhao, Z. Guo, Appl. Phys. Lett. 122 , 042101 [7 pages] (2023). [4] J. Qin, B. Sun, G. Zhou, T. Guo, Y. Chen, C. Ke, S. Mao, X. Chen, J. Shao, Y. Zhao, ACS Mater. Lett. 5 , 2197–2215 (2023). [5] Y. Murata, Chem. Record 15 , 557–594 (2015). [6] R. Vishwakarma, R. Monani, A. Hedayatipour, A. Rezaei, Dis. Inter. Things 3 , 2 [17 pages] (2023).

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