用于神经形态计算的新型液体记忆装置。

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Qinyang Fan, Jianyu Shang, Xiaoxuan Yuan, Zhenyu Zhang, Jingjie Sha
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引用次数: 0

摘要

为了模拟人脑的神经功能,开发与人类神经系统具有天然相似性的硬件对于实现神经形态计算架构至关重要。由于其模拟人工神经元和突触的能力,记忆电阻器被广泛认为是实现神经形态计算的主要候选者。然而,目前大多数忆阻器器件都是固态的。相比之下,生物神经系统在水环境中运行,人脑通过调节神经元细胞中的离子转运来完成信息产生、传递和记忆等智能行为。为了实现更类似于生物系统和更节能的计算系统,基于液体环境的忆阻器器件被开发出来。与传统的固态忆阻器相比,液体基忆阻器具有抗干扰、低能耗、低发热量等优点。同时,它们表现出良好的生物相容性,使它们成为下一代人工智能系统的理想选择。许多基于液体的记忆电阻器的实验证明,展示了其独特的记忆特性和新的神经形态功能。本文综述了近年来液体基忆阻器的研究进展,讨论了液体基忆阻器的工作机理、结构和功能特性。展望了液体记忆电阻器在神经形态计算系统中的应用前景和发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emerging Liquid-Based Memristive Devices for Neuromorphic Computation.

To mimic the neural functions of the human brain, developing hardware with natural similarities to the human nervous system is crucial for realizing neuromorphic computing architectures. Owing to their capability to emulate artificial neurons and synapses, memristors are widely regarded as a leading candidate for achieving neuromorphic computing. However, most current memristor devices are solid-state. In contrast, biological nervous systems operate within an aqueous environment, and the human brain accomplishes intelligent behaviors such as information generation, transmission, and memory by regulating ion transport in neuronal cells. To achieve computing systems that are more analogous to biological systems and more energy-efficient, memristor devices based on liquid environments are developed. In contrast to traditional solid-state memristors, liquid-based memristors possess advantages such as anti-interference, low energy consumption, and low heat generation. Simultaneously, they demonstrate excellent biocompatibility, rendering them an ideal option for the next generation of artificial intelligence systems. Numerous experimental demonstrations of liquid-based memristors are reported, showcasing their unique memristive properties and novel neuromorphic functionalities. This review focuses on the recent developments in liquid-based memristors, discussing their operating mechanisms, structures, and functional characteristics. Additionally, the potential applications and development directions of liquid-based memristors in neuromorphic computing systems are proposed.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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