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Effects of the voltage ramp rate on the conduction characteristics of HfO2-based resistive switching devices

重置(财务) 记忆电阻器 电压 神经形态工程学 材料科学 电阻随机存取存储器 电导 电阻式触摸屏 光电子学 电子线路 电气工程 热传导 计算机科学 凝聚态物理 物理 工程类 机器学习 人工神经网络 金融经济学 经济 复合材料 冶金
作者
H. García,Guillermo Vinuesa,E. García-Ochoa,Fernando Aguirre,Mireia Bargalló González,F. Jiménez-Molinos,F. Campabadal,J.B. Roldán,E. Miranda,S. Dueñas,Helena Castán
出处
期刊:Journal of Physics D [Institute of Physics]
卷期号:56 (36): 365108-365108 被引量:11
标识
DOI:10.1088/1361-6463/acdae0
摘要

Abstract Memristive devices have shown a great potential for non-volatile memory circuits and neuromorphic computing. For both applications it is essential to know the physical mechanisms behind resistive switching; in particular, the time response to external voltage signals. To shed light in these issues we have studied the role played by the applied voltage ramp rate in the electrical properties of TiN/Ti/HfO 2 /W metal–insulator–metal resistive switching devices. Using an ad hoc experimental set-up, the current–voltage characteristics were measured for ramp rates ranging from 100 mV s −1 –1 MV s −1 . These measurements were used to investigate in detail the set and reset transitions. It is shown that the highest ramp rates allow controlling the resistance values corresponding to the intermediate states at the very beginning of the reset process, which is not possible by means of standard quasistatic techniques. Both the set and reset voltages increase with the ramp rate because the oxygen vacancies movement is frequency dependent so that, when the ramp rate is high enough, the conductive filaments neither fully form nor dissolve. In agreement with Chua’s theory of memristive devices, this effect causes the device resistance window to decrease as the ramp rate increases, and even to vanish for very high ramp rates. Remarkably, we demonstrate that the voltage ramp rate can be straightforwardly used to control the conductance change of the switching devices, which opens up a new way to program the synaptic weights when using these devices to mimic synapses for neuromorphic engineering applications. Moreover, the data obtained have been compared with the predictions of the dynamic memdiode model.
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