Planar Nanofluidic Memristors Enabled by Surface Charge Gradient

Abstract

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.