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arXiv 2408.09780physics.app-phcond-mat.mtrl-sci
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用于人工神经元应用的具有横向银离子迁移的挥发性MoS${_2}$忆阻器

Volatile MoS${_2}$ Memristors with Lateral Silver Ion Migration for Artificial Neuron Applications

Sofia Cruces, Mohit D. Ganeriwala, Jimin Lee, Lukas Völkel, Dennis Braun, Annika Grundmann, Ke Ran, Enrique G. Marín, Holger Kalisch, Michael Heuken, Andrei Ves… 展开作者

Sofia Cruces, Mohit D. Ganeriwala, Jimin Lee, Lukas Völkel, Dennis Braun, Annika Grundmann, Ke Ran, Enrique G. Marín, Holger Kalisch, Michael Heuken, Andrei Vescan, Joachim Mayer, Andrés Godoy, Alwin Daus, Max C. Lemme

AI总结:

本文提出基于MOCVD生长多层MoS${_2}$的横向银离子迁移挥发性忆阻器,实现无forming、低电压、快速且可重复的电阻开关,并建立物理紧凑模型,探索其作为人工神经元在神经形态系统中的应用。

摘要:

层状二维(2D)半导体在其范德华(vdW)间隙和表面上表现出增强的离子迁移能力。这一效应可用于新兴存储器、选择器和神经形态计算器件中的电阻开关(RS)。迄今为止,所有基于横向二硫化钼(MoS${_2}$)的银(Ag)离子迁移挥发性RS器件均采用剥离的单晶MoS${_2}$薄片实现,并且需要forming步骤才能实现RS。在此,我们展示了采用金属有机化学气相沉积(MOCVD)生长的多层MoS${_2}$实现的挥发性RS,具有可重复的无forming操作。该器件表现出高度可重复的挥发性RS,工作电压低至约2 V,考虑到其微米级尺寸,开关时间快至130 ns。我们通过透射电子显微镜、电子输运建模和密度泛函理论研究了基于Ag离子表面迁移的开关机制。最后,我们开发了一个基于物理的紧凑模型,并探索了将我们的挥发性忆阻器作为人工神经元在神经形态系统中实现的应用。

英文摘要:

Layered two-dimensional (2D) semiconductors have shown enhanced ion migration capabilities along their van der Waals (vdW) gaps and on their surfaces. This effect can be employed for resistive switching (RS) in devices for emerging memories, selectors, and neuromorphic computing. To date, all lateral molybdenum disulfide (MoS${_2}$)-based volatile RS devices with silver (Ag) ion migration have been demonstrated using exfoliated, single-crystal MoS${_2}$ flakes requiring a forming step to enable RS. Here, we present volatile RS with multilayer MoS${_2}$ grown by metal-organic chemical vapor deposition (MOCVD) with repeatable forming-free operation. The devices show highly reproducible volatile RS with low operating voltages of approximately 2 V and fast switching times down to 130 ns considering their micrometer scale dimensions. We investigate the switching mechanism based on Ag ion surface migration through transmission electron microscopy, electronic transport modeling, and density functional theory. Finally, we develop a physics-based compact model and explore the implementation of our volatile memristors as artificial neurons in neuromorphic systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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