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Forming-Free NbO<sub> <i>x</i> </sub>-Based Memristor Enabling Low-Energy-Consumption Artificial Spiking Afferent Nerves

Yaxin Ding, Peng Yuan, Jie Yu, Yuting Chen, Pengfei Jiang, Yuan Wang, Yannan Xu, Shuxian Lv, Zhiwei Dang, Boping Wang, Xiaoxin Xu, Tiancheng Gong, Qing Luo

2022IEEE Transactions on Electron Devices19 citationsDOI

Abstract

Two-terminal volatile NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> -based threshold switching memristor devices with electrical self-oscillation behavior have attracted tremendous interest for applications in both oscillators and neural networks. However, the forming process of NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> devices can be a burden and limit their application. In this study, a forming-free NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> -based threshold switching device is obtained by reducing the oxygen content of the NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> film, which simplifies the circuit design of the NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> device array and improves the device-to-device (D2D) uniformity due to the removal of the forming step. Furthermore, a low-threshold switching voltage is achieved in the forming-free device, indicating a low-power consumption operation. Finally, we demonstrate a low-energy-consumption artificial spiking afferent nerve based on a forming-free NbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> device for spiking neural network (SNN) applications.

Topics & Concepts

Natural bond orbitalTopology (electrical circuits)Computer sciencePhysicsElectrical engineeringEngineeringQuantum mechanicsDensity functional theoryAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering
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