Self‐Powered Artificial Neuron Devices: Towards the All‐In‐One Perception and Computation System
Tong Zheng, Xinkai Xie, Qiongfeng Shi, Jun Wu, Cunjiang Yu
Abstract
The increasing demand for energy supply in sensing units and the computational efficiency of computation units has prompted researchers to explore novel, integrated technology that offers high efficiency and low energy consumption. Self-powered sensing technology enables environmental perception without external energy sources, while neuromorphic computation provides energy-efficient and high-performance computing capabilities. The integration of self-powered sensing technology and neuromorphic computation presents a promising solution for an all-in-one system. This review examines recent developments and advancements in self-powered artificial neuron devices based on triboelectric, piezoelectric, and photoelectric effects, focusing on their structures, mechanisms, and functions. Furthermore, it compares the electrical characteristics of various types of self-powered artificial neuron devices and discusses effective methods for enhancing their performance. Additionally, this review provides a comprehensive summary of self-powered perception systems, encompassing tactile, visual, and auditory perception systems. Moreover, it elucidates recently integrated systems that combine perception, computing, and actuation units into all-in-one configurations, aspiring to realize closed-loop control. The seamless integration of self-powered sensing and neuromorphic computation holds significant potential for shaping a more intelligent future for humanity.