Synaptic Transistors Using Scalable Graphene Nanoribbons.

ABSTRACT

Graphene has demonstrated potential for use in neuromorphic electronics due to its superior electrical properties. However, these devices are all based on graphene sheets without patterning, restricting its applications. Here, we demonstrate a graphene nanoribbon synaptic transistor (GNST), with the graphene nanoribbon (GNR) channels fabricated using an electro-hydrodynamically printed nanowire array as lithographic masks for scalable fabrication. The GNST shows tunable synaptic plasticity by spike duration, frequency, and number. Moreover, the device is energy-efficient and ambipolar and shows a regulated response by nanoribbon width. The characteristics of GNSTs are applicable to pattern recognition, showing an accuracy of 84.5%. The device is applicable to Pavlov's classical conditioning. This study reports the first synaptic transistor based on GNRs, providing new insights into future neuromorphic electronics.