Zaheer Muhammad, Bacha Aziz-Ur-Rahim, Nabi Iqra, Lan Jun, Wang Wenhui, Shen Mei, Chen Kai, Zhang Guobiao, Zhou Feichi, Lin Longyang, Irshad Muhammad, Faridullah Faridullah, Arifeen Awais, Li Yida
School of Microelectronics, Southern University of Science and Technology, Shenzhen518055, China.
Department of Environmental Science and Engineering, Fudan University, Shanghai200433, China.
ACS Omega. 2022 Oct 5;7(45):40911-40919. doi: 10.1021/acsomega.2c03893. eCollection 2022 Nov 15.
Herein, we report a solution-processable memristive device based on bismuth vanadate (BiVO) and titanium dioxide (TiO) with gallium-based eutectic gallium-indium (EGaIn) and gallium-indium-tin alloy (GaInSn) liquid metal as the top electrode. Scanning electron microscopy (SEM) shows the formation of a nonporous structure of BiVO and TiO for efficient resistive switching. Additionally, the gallium-based liquid metal (GLM)-contacted memristors exhibit stable memristor behavior over a wide temperature range from -10 to +90 °C. Gallium atoms in the liquid metal play an important role in the conductive filament formation as well as the device's operation stability as elucidated by - characteristics. The synaptic behavior of the GLM-memristors was characterized, with excellent long-term potentiation (LTP) and long-term depression (LTD) linearity. Using the performance of our device in a multilayer perceptron (MLP) network, a ∼90% accuracy in the handwriting recognition of modified national institute of standards and technology database (MNIST) was achieved. Our findings pave a path for solution-processed/GLM-based memristors which can be used in neuromorphic applications on flexible substrates in a harsh environment.
在此,我们报道了一种基于钒酸铋(BiVO)和二氧化钛(TiO)的可溶液加工忆阻器件,其顶部电极采用镓基共晶镓铟(EGaIn)和镓铟锡合金(GaInSn)液态金属。扫描电子显微镜(SEM)显示BiVO和TiO形成了无孔结构,以实现高效的电阻切换。此外,基于镓的液态金属(GLM)接触的忆阻器在从-10到+90°C的宽温度范围内表现出稳定的忆阻器行为。液态金属中的镓原子在导电细丝形成以及器件的操作稳定性方面起着重要作用,正如-特性所阐明的那样。对GLM忆阻器的突触行为进行了表征,具有出色的长时程增强(LTP)和长时程抑制(LTD)线性。利用我们的器件在多层感知器(MLP)网络中的性能,在修改后的国家标准与技术研究所数据库(MNIST)的手写识别中实现了约90%的准确率。我们的研究结果为基于溶液加工/GLM的忆阻器铺平了道路,这种忆阻器可用于恶劣环境下柔性基板上的神经形态应用。