Rippled graphene pores as fluidic memristive devices with synaptic and neuromorphic functionalities

TL;DR

This study demonstrates that micrometer-sized rippled graphene pores can exhibit ionic memory effects, enabling scalable, stable, and neuromorphic-like fluidic memristive devices with synaptic functionalities.

Contribution

It reveals that pore rim morphology, rather than nanoconfinement, can induce ionic memory effects, simplifying device fabrication and enhancing scalability for neuromorphic applications.

Findings

Rippled graphene pores show long-term ionic memory effects.

Devices can be reversibly modulated using voltage spikes and electrolytes.

Successful demonstration of image recognition and neural signal analysis.

Abstract

Nanofluidic memristive devices work with nanoscale pores and ions dissolved in water, which harness the ionic memory effect aiming to store and process information. These devices share the same charge carriers as biological systems and bring hope for better emulating the neural functions and developing ionic circuits for neuromorphic applications. Specially, theory and experiments suggest that nanoconfinement is essential for inducing a memory effect, which places limit on the pore size to nm-scale or smaller. Such devices are difficult to scale up with precision and operate with long-term stability. Here, we show that a micrometer size pore, generally expected to exhibit a linear ion transport, can display a pronounced memory effect, if its rim is wrapped by strongly curved and tightly stacked graphene. We attribute the observation to slow ion dynamics confined in the rippled graphene edges. The devices are easy to scale up and integrate into fluidic circuits. The memory effect is ion-selective and exhibits long endurance comparable to the lifetime of synaptic proteins, which enables reversible modification of the conductance states using programmable voltage spikes and various electrolytes over a long time, akin to biological synaptic plasticity. Thanks to this plasticity, our devices and their integrated circuits enable storing, transmitting and processing information with high reliability, fidelity and accuracy, as evidenced in the identification of both greyscale and color images, and in the real-time analysis of emulated neural signals. Our results highlight nanoscale morphology of the pore wall as an important parameter regulating ion transport and indicate that the stringent nanoconfinement for ionic memory can be lifted from restricting the pore size to designing its rim structure. The devices and their integrated circuits may find use in ionic neuromorphic applications.