Reliability of analog resistive switching memory for neuromorphic computing

Abstract

As artificial intelligence calls for novel energy-efficient hardware, neuromorphic computing systems based on analog resistive switching memory (RSM) devices have drawn great attention recently. Different from the well-studied binary RSMs, the analog RSMs are featured by a continuous and controllable conductance-tuning ability and thus are capable of combining analog computing and data storage at the device level. Although significant research achievements on analog RSMs have been accomplished, there have been few works demonstrating large-scale neuromorphic systems. A major bottleneck lies in the reliability issues of the analog RSM, such as endurance and retention degradation and read/write noises and disturbances. Owing to the complexity of resistive switching mechanisms, studies on the origins of reliability degradation and the corresponding optimization methodology face many challenges. In this article, aiming on the high-performance neuromorphic computing applications, we provide a comprehensive review on the status of reliability studies of analog RSMs, the reliability requirements, and evaluation criteria and outlook for future reliability research directions in this field.