Self-Powered Memristive Systems for Storage and Neuromorphic Computing - Shi, Jiajuan; Wang, Zhongqiang; Tao, Ye; Xu, Haiyang; Zhao, Xiaoning; Lin, Ya; Liu, Yichun (Frontiers in Neuroscience, 2021)
Neuromorphic computing chip that can imitate the human brain’s ability of processing multiple types of data simultaneously, is promising to fundamentally innovate the criticized von-neumann computer architecture. Memristive devices, thanks to their inherent low voltage operation, multi-bit storage and cost-effective manufacturability, have been confirmed to be one of the most excellent hardware units for building neuromorphic intelligence systems. However, as a passive device, memristor cell definitely needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, has the potential to perfectly solve the above problems. It has attracted great interest by capturing advantage of some power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. Meanwhile, a perspective is given about the artificial intelligence application with self-powered memristive system.