Overshoot-Suppressed Memristor Array with AlN Oxygen Barrier for Low-Power Operation in the Intelligent Neuromorphic Systems

Abstract

As the demand for bio-inspired neuromorphic systems grows, memristor has emerged as a pivotal component in artificial synaptic devices. This study delves into the advantages and limitations of the one-transistor-one-resistor (1T-1R) and 0T-1R architectures for memory array configurations. A significant enhancement in the memristor's on/off ratio, surpassing 103, achieved by integrating both an overshoot suppression layer (OSL) and an ultra-thin AlN oxygen barrier layer (OBL) is also reported. The concurrent insertion of an AlOx OSL is essential for manifesting the self-compliance attribute in 4F2 memristor arrays. Evaluations of a 24 x 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. In terms of functionality, a 7.13-fold decline in vector-matrix multiplication error is observed, accentuating the potential of this approach for neural network synapse applications. The analog reset characteristics of the OBL memristor facilitate over 25 multilevel states. Furthermore, the adoption of nonnegative weights presents an avenue to potentially double synaptic integration density. Through simulation program with integrated circuit emphasis simulations, the necessity of nonnegative and 16-level quantized conductance in balancing power consumption without accuracy loss at the image classification is validated. A significant enhancement in the memristor's on/off ratio over 103 by integrating both an overshoot suppression layer (OSL) and an ultra-thin AlN oxygen barrier layer (OBL) is reported. Evaluations of a 24 x 24 array embedded with OSL and OBL reveal a substantial reduction in retention variation. A 7.13-fold decline in vector-matrix multiplication error is demonstrated, accentuating the potential of this approach for neural network and synapse applications.image (c) 2024 WILEY-VCH GmbH