用于 RRAM 和神经形态应用的碳基记忆晶体管

Chip Pub Date : 2024-02-01 DOI:10.1016/j.chip.2024.100086
Fan Yang , Zhaorui Liu , Xumin Ding , Yang Li , Cong Wang , Guozhen Shen
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引用次数: 0

摘要

作为纳米材料的典型代表,碳纳米材料以其独特的物理化学性质、多维性、多杂化方法和优异的电子特性,在电子器件的构建中受到广泛关注。特别是近年来,基于碳纳米材料的忆阻器在构建非易失性存储器件和神经形态应用领域蓬勃发展。本文系统综述了不同尺寸碳纳米材料的制备方法和结构特征。然后,深入讨论了基于不同尺寸碳纳米材料的忆阻器的结构特征和工作机理。最后,介绍了基于碳纳米材料的忆阻器在逻辑运算、神经网络构建、人工视觉系统、人工触觉系统和多模态感知系统中的潜在应用。我们相信,本文将为未来基于碳基忆阻器的高质量信息存储、高性能神经形态应用和高灵敏度仿生传感的发展提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Carbon-based memristors for resistive random access memory and neuromorphic applications

As a typical representative of nanomaterials, carbon nanomaterials have attracted widespread attention in the construction of electronic devices owing to their unique physical and chemical properties, multi-dimensionality, multi-hybridization methods, and excellent electronic properties. Especially in the recent years, memristors based on carbon nanomaterials have flourished in the field of building non-volatile memory devices and neuromorphic applications. In the current work, the preparation methods and structural characteristics of carbon nanomaterials of different dimensions were systematically reviewed. Afterwards, in depth discussion on the structural characteristics and working mechanism of memristors based on carbon nanomaterials of different dimensions was conducted. Finally, the potential applications of carbon-based memristors in logic operations, neural network construction, artificial vision systems, artificial tactile systems, and multimodal perception systems were also introduced. It is believed that this paper will provide guidance for the future development of high-quality information storage, high-performance neuromorphic applications, and high-sensitivity bionic sensing based on carbon-based memristors.

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