Hu Chao, Liang Lijuan, Yu Jinran, Cheng Liuqi, Zhang Nianjie, Wang Yifei, Wei Yichen, Fu Yixuan, Wang Zhong Lin, Sun Qijun
School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102627, P. R. China.
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, P. R. China.
Cyborg Bionic Syst. 2025 Apr 22;6:0256. doi: 10.34133/cbsystems.0256. eCollection 2025.
In recent years, the rapid progression of artificial intelligence and the Internet of Things has led to a significant increase in the demand for advanced computing capabilities and more robust data storage solutions. In light of these challenges, neuromorphic computing, inspired by human brain's architecture and operation principle, has surfaced as a promising answer to the growing technological demands. This novel methodology emulates the biological synaptic mechanisms for information processing, enabling efficient data transmission and computation at the identical position. Two-dimensional (2D) materials, distinguished by their atomic thickness and tunable physical properties, exhibit substantial potential in emulating synaptic plasticity and find broad applications in neuromorphic computing. With respect to device architecture, memory devices based on floating-gate (FG) structures demonstrate robust data retention capabilities and have been widely used in the realm of flash memory. This review begins with a succinct introduction to 2D materials and FG transistors, followed by an in-depth discussion on remarkable research progress in the integration of 2D materials with FG transistors for applications in neuromorphic computing and memory. This paper offers a thorough review of the existing research landscape, encapsulating the notable progress in swiftly expanding field. In conclusion, it addresses the constraints encountered by FG transistors using 2D materials and delineates potential future trajectories for investigation and innovation within this area.
近年来，人工智能和物联网的迅速发展导致对先进计算能力和更强大数据存储解决方案的需求大幅增加。鉴于这些挑战，受人类大脑架构和运作原理启发的神经形态计算已成为应对不断增长的技术需求的一个有前景的答案。这种新颖的方法模拟了用于信息处理的生物突触机制，能够在同一位置实现高效的数据传输和计算。二维（2D）材料以其原子厚度和可调节的物理特性而著称，在模拟突触可塑性方面具有巨大潜力，并在神经形态计算中有着广泛应用。在器件架构方面，基于浮栅（FG）结构的存储器件展现出强大的数据保持能力，并已在闪存领域得到广泛应用。本综述首先简要介绍二维材料和FG晶体管，随后深入讨论二维材料与FG晶体管集成在神经形态计算和存储器应用方面的显著研究进展。本文全面回顾了现有研究概况，总结了这个迅速发展领域的显著进展。最后，阐述了使用二维材料的FG晶体管所面临的限制，并描绘了该领域未来潜在的研究和创新方向。
