Experimental demonstration and analysis of crossbar array memristor for brain-inspired computing

Abstract

The human brain accomplishes intricate computational tasks like learning, recognition, and cognition with minimal power usage, and showcases synaptic behavior well-suited for neuromorphic computing systems. The latest advancements in memristive devices are pivotal for designing memory and synapses, as well as for non-volatile data storage and computing. However, a directive on realizing memristive analog behavior is still a major concern. Here we propose forming free digital and analog resistive switching (ARS) dynamics of 4 × 4 crossbar array MgZnO-based memristive devices for neural activity (Potentiation/Depression). Moreover, our device demonstrates both abrupt and gradual resistive switching (RS) behaviors, with acceptable endurance (10 K cycles at 85 °C), data retention (107 s), a read voltage range of 0.1 V to 0.5 V, and pulse widths ranging from 50 μs to 250 μs (with a pulse interval of 50 μs) for synaptic behavior for neuromorphic computing. Our approach deals with multifunctional memory and synaptic behavior with low voltage linearity and excellent switching characteristics. Our results highlight the potential of memristor for energy-efficient edge computing and brain-inspired computing systems.