Characterization of Graphene/Ionic Liquid Memristive Devices for Neuromorphic Systems

TL;DR

This paper explores flexible graphene/ionic liquid memristive devices on polymer substrates, demonstrating their potential for neuromorphic systems through electrical double layer formation, conductance modulation, and associative learning capabilities.

Contribution

It introduces novel flexible graphene/ionic liquid memristive devices, investigates their electrical properties, and demonstrates their ability to emulate associative learning in bioelectronic applications.

Findings

Voltage pulse trains increase device conductance.

Devices exhibit memristive switching behavior.

Potential for neuromorphic and bioelectronic applications.

Abstract

Flexible and biocompatible memristive devices are particularly attractive for bioelectronic systems due to the interest in improving computing capabilities and the motivation to interface electronics with biological systems including drug delivery, neural interfaces and biosensors. Structures made of more unorthodox, organic material can address different issues due to their characteristics: flexibility, conformability, biocompatibility and simple and low-cost fabrication. It has been observed that gating graphene/ionic liquid (IL) devices leads to the formation of an electrical double layer (a thin layer of ions with a thickness of a few nanometers) at the graphene/IL interface due to the local potential difference which also controls the local conductivity. This structure provides a memristive mechanism based on a dynamic p-n junction formation along the channel. Motivated by this memristive behavior, graphene/IL devices were assembled with the aim of demonstrating memristive behavior and associative learning. This work investigates memristive properties of flexible graphene/ IL devices on polymer substrates. The I-V characteristics of these novel devices and switching mechanism are investigated. Two distinct topologies (single input, single output and double input, single output) of devices are manufactured and tested to mimic conditioning. It is observed that the application of voltage pulse trains of both positive and negative polarities increases the device conductance and allows larger currents to pass after repetitive excitation. This characteristic was exploited to condition devices and emulate associative learning.