Thermal design engineering for improving the variation of memristor threshold

Xing Li, Jianxun Zou, Zhe Feng, Zuheng Wu, Zuyu Xu, Fei Yang, Yunlai Zhu and Yuehua Dai

Chaos, Solitons & Fractals, 2023, vol. 171, issue C

Abstract: Memristor-based neural components and circuits are the key to building efficient brain-like computing systems, but their development is restricted by their unstable electrical properties of memristive artificial neurons. In this work, three thermal design schemes to regulate the electrical characteristics of threshold switching memristor were proposed, such as the thermal conductivity of the device electrode, the different substrate and local electrode. First, it was found that the lower the thermal conductivity of electrode, the lower the threshold variation of the device. Secondly, the smaller the thermal conductivity of the memristor substrate, the more stable the threshold. Thirdly, decreasing the electrode radius of the memristor from the original 50 nm to 10 nm will decrease the variation of threshold voltage by 27%. Additionally, due to the reduction of thermal dissipation, the proposed thermal design engineering also can lower the threshold voltage and power consumption. Those results on improving the threshold variation of memristor provide a stepping stone for the development of artificial neurons and neuromorphic computing systems.

Keywords: Memristor; Finite element simulations; Thermal conductivity; Threshold variation; Thermal design engineering (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:171:y:2023:i:c:s0960077923003673

DOI: 10.1016/j.chaos.2023.113466

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