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Neuromorphic devices have gained significant attention as potential buildingblocks for the next generation of computing technologies owing to their abilityto emulate the functionalities of biological nervous systems. The essentialcomponents in artificial neural network such as synapses and neurons arepredominantly implemented by dedicated devices with specific functionalities.In this work, we present a gate-controlled transition of neuromorphic functionsbetween artificial neurons and synapses in monolayer graphene transistors thatcan be employed as memtransistors or synaptic transistors as required. Byharnessing the reliability of reversible electrochemical reactions between Catoms and hydrogen ions, the electric conductivity of graphene transistors canbe effectively manipulated, resulting in high on/off resistance ratio,well-defined set/reset voltage, and prolonged retention time. Overall, theon-demand switching of neuromorphic functions in a single graphene transistorprovides a promising opportunity to develop adaptive neural networks for theupcoming era of artificial intelligence and machine learning.

Gate-controlled neuromorphic functional transition in an electrochemical graphene transistor