Interface Ion-Driven, Highly Stable Synaptic Memristor for Neuromorphic Applications

Abstract

Because of the continual advancements in the artificial intelligence technology, its practical applications (e.g., in computer vision, health care, and pattern recognition) are expanding. Various architectures integrating memory cells and transistors have been used to demonstrate artificial synaptic arrays. However, designing memory cells with superior analogue switching is a major challenge in the development of neuromorphic systems. We evaluated a TiN/AlN/Cu/AlN/Pt memristive synapse for neuromorphic computing. The synaptic device exhibited multilevel characteristics with varying reset stop voltages ranging from −2.7 to −2.2 V. The device also exhibited highly stable reparative 200 potentiation and depression cycles with high nonlinearity values of approximately 2.39 for potentiation and −2.19 for depression. The device further exhibited highly stable DC endurance over 1000 cycles, AC pulse endurance (1 M), and stable retention (104 s) at 100 °C without any degradation. The experimental potentiation and depression data were used to train a Hopfield neural network to effectively identify input images with a resolution of 28 × 28 pixels, representing 784 synapses used in the simulation process. The training resulted in an accuracy of >91% in 28 epochs. Therefore, the newly designed device exhibits promising features, such as high linearity and accuracy, and is thus highly suitable for neuromorphic computing purposes.