RT info:eu-repo/semantics/article
T1 Effects of the voltage ramp rate on the conduction characteristics of HfO2-based resistive switching devices
A1 Aguirre, F L
A1 González, M B
A1 Jiménez-Molinos, F
A1 Campabadal, F
A1 Roldán, J B
A1 Miranda, E
A1 Castán Lanaspa, María Helena
A1 Dueñas Carazo, Salvador
A1 García García, Héctor
A1 García-Ochoa, E.
A1 Vinuesa Sanz, Guillermo
K1 Resistive switching
K1 Electrical characterization
K1 Memristor
K1 Memdiode model
K1 2203 Electrónica
K1 3307.90 Microelectrónica
AB 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/HfO2/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.
PB IOP Publishing
SN 0022-3727
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/73799
UL https://uvadoc.uva.es/handle/10324/73799
LA eng
NO Journal of Physics D: Applied Physics, June 2023,  Volume 56, Number 36, p.365108 (13pp)
NO Producción Científica
DS UVaDOC
RD 22-ene-2025