Gate-controlled neuromorphic functional transition in an electrochemical graphene transistor

Neuromorphic devices have gained significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural network such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between C atoms and hydrogen ions, the electric conductivity of graphene transistors can be effectively manipulated, resulting in high on/off resistance ratio, well-defined set/reset voltage, and prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity to develop adaptive neural networks for the upcoming era of artificial intelligence and machine learning.