Engineering synaptic plasticity through the control of oxygen vacancy concentration for the improvement of learning accuracy in a Ta2O5 memristor

Abstract

Ta2O5 memristors exhibit bipolar switching properties attributable to the growth and destruction of oxygen vacancy filaments (OVFs). The transmission properties of biological synapse are mimicked in these memristors. The Ta2O5 memristor that contains numerous oxygen vacancies (OVs) is heated under N-2 at 10 Torr, and it shows high conductance modulation linearity (CML) because the variation of OW is governed by the redox reaction. The recognition accuracy of artificial neural networks (ANNs) is affected significantly by the CML of the memristor. Simulation using a convolutional neural network reveals that this Ta2O5 memristor exhibits a high learning accuracy of 93% because of its high CML. Spike-timing-dependent plasticity (STDP) was realized in Ta2O5 memristors. The change rate of synaptic weight variation in the STDP curve, which is also related to the learning accuracy of ANNs, is large in the Ta2O5 memristor heated under N-2 at 10 Torr; this confirms that this memristor has a good learning accuracy. Spike rate-dependent plasticity and the transition from short-term plasticity to long-term plasticity are observed in Ta2O5 memristors. Further, they were obtained at a small potentiation spike in a Ta2O5 memristor heated under N-2 at 10 Torr because numerous OVs exist in this memristor. (C) 2022 Published by Elsevier B.V.