Interfacial Switching-Based, Bioinspired, Highly Stable, and Reliable Synapse for Neuromorphic Applications

In this study, an interfacial switching (IFS)-based bioinspired TiN/AlON/TaON/Pt resistive random-access memory (RRAM) device was fabricated to investigate its conduction mechanism and synaptic behavior for neuromorphic computing. This device exhibited excellent dc endurance over at least 10000 cycles, an ac pulse endurance of 1 M, and long-term retention (10 6 s) at 150 °C with no degradation. The device also showed multilevel characteristics. The RESET stop voltage of the device varied from −0.9 to −1.3 V. The device was highly stable over 250 potentiation and depression cycles, which are crucial in Hopfield neural network (HNN)-based neuromorphic systems. High nonlinearity (1.13 for potentiation and −1.75 for depression) was achieved using the device potentiation and depression functions. Experimental potentiation and depression data were used to train an HNN based on the fabricated device to recognize an input image of 28 × 28 pixels that contained 784 synapses. The HNN had a training accuracy of higher than 93% in 22 iterations. The experimental results indicate that the fabricated IFS-based TiN/AlON/TaON/Pt RRAM device is highly suitable for neuromorphic devices that mimic synaptic characteristics for neuromorphic systems.