Xiao Yongyue, Jiang Bei, Zhang Zihao, Ke Shanwu, Jin Yaoyao, Wen Xin, Ye Cong
Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China.
Faculty of Chemical Technology and Engineering, West Pomeranian University of Technology in Szczecin, Szczecin, Poland.
Sci Technol Adv Mater. 2023 Feb 28;24(1):2162323. doi: 10.1080/14686996.2022.2162323. eCollection 2023.
With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.
随着人工智能(AI)的蓬勃发展,基于互补金属氧化物半导体器件的传统冯·诺依曼计算架构正面临内存墙和功耗墙问题。基于忆阻器的内存计算有潜力克服当前计算机的瓶颈并实现硬件突破。在本综述中,总结了存储器件在材料与结构设计、器件性能及应用方面的最新进展。介绍了包括电极、二元氧化物、钙钛矿、有机物和二维材料在内的各种电阻开关材料,并讨论了它们在忆阻器中的作用。随后,分析了异形电极的构建、功能层的设计以及其他影响器件性能的因素。我们重点关注电阻的调制以及提高性能的有效方法。此外,还介绍了突触可塑性、光电特性、在逻辑运算和模拟计算中的前沿应用。最后,讨论了一些关键问题,如电阻开关机制、多感官融合、系统级优化等。