AI 中文总结
本文提出一种由表面电荷梯度实现的平面纳米流体忆阻器，采用PNP框架分析其忆阻行为，揭示记忆效应与表面电荷一阶矩的关联，为该类器件的优化设计提供理论基础。
AI 中文摘要
纳米流体忆阻器利用纳米通道中的离子输运，在神经形态应用中具有应用前景。平面架构对于与成熟的微纳加工技术实现可扩展集成尤为重要。本文采用泊松-能斯特-普朗克（Poisson-Nernst-Planck, PNP）框架，从理论上提出了由表面电荷梯度实现的平面纳米流体忆阻器，为常用的几何不对称架构提供了替代方案。所产生的忆阻行为由扩散介导的二次富集效应调控。通过系统求解PNP方程，我们得到了参数空间中特征记忆时间的标度关系，还揭示了对于任意电荷分布，记忆效应与表面电荷的一阶矩相关。这些结果为通过空间图案化表面电荷合理设计和优化平面纳米流体忆阻器提供了理论基础。
英文摘要
Nanofluidic memristors, exploiting ion transport in nanochannels, hold promise for neuromorphic applications. A planar architecture is particularly desired for scalable integration with established micro- and nanofabrication technologies. Here, using the Poisson-Nernst-Planck framework, we theoretically propose planar nanofluidic memristors enabled by surface charge gradient, providing an alternative to the commonly used geometrically asymmetric architectures. The resulting memristive behavior is governed by a diffusion-mediated secondary enrichment effect. By systematically solving the PNP equations, we obtain the scaling of the characteristic memory time across the parameter space. We also reveal that the memory effect is related to the first-order moment of surface charge, for arbitrary charge profiles. These results provide a theoretical basis for rationally designing and optimizing planar nanofluidic memristors through spatially patterned surface charge.
CommentsMain text: 7 pages, 4 figures. Supplementary material: 17 pages, 13 figures
