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

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 N₂ at 10 Torr, and it shows high conductance modulation linearity (CML) because the variation of OVF 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 N₂ 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 N₂ at 10 Torr because numerous OVs exist in this memristor.