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Theoretical investigation of the Ag filament morphology in conductive bridge random access memories

机译:导电桥随机存取存储器中Ag细丝形态的理论研究

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Conductive bridge random access memories (CBRAMs) usually involve active Ag or Cu metals, where the formation of metal filaments accounts for the low resistance state. For the application of neuromorphic computation, it is highly desirable to develop artificial neurons and synapses, which utilize the complicated volatile or nonvolatile resistive switching phenomena, respectively. This can be achieved by controlling the morphology and stability of the filaments, which requires a deep understanding of the filament formation and disruption mechanisms. Using ab initio calculations, we explored the physical mechanism behind various Ag filament morphologies and growth modes, using GeSe, ZrO2, SiO2, and a-Si as the examples. The roles of Ag and Ag+ stability inside the dielectric, the migration barrier of Ag+, and the Ag+ solvation effect have been investigated in detail. A comprehensive model has been proposed, which in particular could explain the diverse Ag filament morphology experimentally observed in sputtered SiO2 and PECVD SiO2. Our theoretical approach can serve as a pre-screening method in designing new solid-state electrolyte materials of CBRAM, aiming at new functionalities in neuromorphic computation or in-memory logic computing. Published by AIP Publishing.
机译:导电桥随机存取存储器(CBRAM)通常包含活性Ag或Cu金属,其中金属丝的形成说明了低电阻状态。对于神经形态计算的应用,非常需要开发分别利用复杂的易失性或非易失性电阻切换现象的人工神经元和突触。这可以通过控制细丝的形态和稳定性来实现,这需要深入了解细丝的形成和破坏机理。通过从头算,我们以GeSe,ZrO2,SiO2和a-Si为例,探索了各种Ag丝形态和生长模式背后的物理机理。详细研究了Ag和Ag +在电介质内部的稳定性,Ag +的迁移势垒以及Ag +的溶剂化作用。已经提出了一个综合模型,该模型尤其可以解释在溅射的SiO2和PECVD SiO2中实验观察到的各种Ag细丝形态。我们的理论方法可以用作设计CBRAM的新型固态电解质材料的预筛选方法,其目的在于神经形态计算或内存逻辑计算中的新功能。由AIP Publishing发布。

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