Sub-Millisecond and Energy-Efficient Electrochemical Synaptic Transistors with a Partially Reduced Graphene Oxide Channel

Abstract

Designed artificial synaptic transistors, which emulate the functions of biological synapses, are intended to achieve information processing and computation, showcasing their promise in advancing artificial intelligence. Herein, we propose a synaptic transistor composed of a partially reduced graphene oxide (prGO) channel and a Nafion electrolyte, operating based on electrochemical reactions of the prGO channel, which are assisted by protons through the Nafion electrolyte. After electrical reduction of a pristine GO channel to the prGO channel by sweeping the drain voltage, the transistor exhibits over 200 distinct conductance states under applications of short gate voltage pulses down to 500 μs width, giving rise to a low energy consumption of 10–50 pJ per gate pulse. Using highly linear and symmetric long-term potentiation and depression characteristics, an image recognition accuracy using an artificial neural network based on a two-layer perceptron model is calculated to be 90%. If gate current pulses are used, the image recognition accuracy further increases to 94%, because of the improved linearity and symmetry of the conductance change. The transistor also exhibits short-term plasticity, such as paired-pulse facilitation and spike-timing-dependent plasticity, with time ranges of less than a few tens of milliseconds. These superior synaptic properties of the Nafion/prGO transistors will offer a remarkable paradigm for the development of neuromorphic computation architectures.