Abstract
This study presents the Al/ZrO(2)/TiO(2)/Al (AZTA) memristor, a device based on a nonfilamentary mechanism. It is designed to simulate artificial synapses for both artificial neural networks and spiking neural networks. The AZTA device exhibits highly linear and symmetrical potentiation and depression under identical pulse operation conditions, and demonstrate spike-timing-dependent plasticity through precise modulation of the shapes of the pre- and postsynaptic spikes. The mechanism for linear potentiation is thoroughly studied by analyzing the trap distribution through temperature-modulated space charge-limited current spectroscopy. The analyzed trap distribution and J-V align well with Mark-Helfrich's model, demonstrating the high reliability of the analysis. An exponential trap distribution model was found in the bandgap of the switching layer, and deep trap levels were filled preferentially under identical voltage pulses. Subsequently, an expression based on this model was proposed to explain linear potentiation for the first time. Finally, a temporal SNN simulation demonstrates that the small nonlinearity factors enable AZTA synapses to excel in classifying the MNIST data set. The best accuracy achieved was 93.7%, which is comparable to that of a perovskite memristor, one of the most linear devices reported. Along with a Gaussian noise analysis, the high uniformity of AZTA results in trivial performance degradation. These findings highlight the potential of the AZTA memristor in practical applications of neuromorphic computing.