Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics.

Abstract

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG's performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na⁺). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics.