Effect of Temperature on Analog Memristor in Neuromorphic Computing

In this article, the influence of the temperature instability of resistive memory switching on potential neuromorphic computing applications is extensively studied using an Intel TaOx-based analog-type memristor as a synaptic weight modulator in a neural network. Evaluation results show that the effect of ambient temperature during training and interference can degrade the neural network's accuracy due to inefficient weight updates and inevitable resistance or conductance drifting. Our results provide additional insights into device-level physical models and simple circuit-level design guidance for potential hardware-based neuromorphic computing applications.