Hybrid Modeling and Sensitivity Analysis on Reduced Graphene Oxide Field-Effect Transistor

The reduced graphene oxide (RGO) field-effect transistor (FET) has been developed and applied in various areas. However, the effective modeling and sensitivity analysis on RGO FET is still a very challenging problem due to the randomness of bandgap and density of states (DOS) in RGO. In this paper, we propose to solve the RGO FET modeling problem by integrating the data-driven thinking and the graphene FET model to develop a hybrid model. The proposed model takes advantages of the similarities between graphene and RGO to generalize the existing graphene FET model, and employs RGO FET drain-current data to characterize the specificity of the model. The basic idea in the proposed model is to modify the graphene DOS to approximate the RGO DOS so that the charge density, mobility and other parameters can be achieved through the approximated RGO DOS. We validate the model accuracy with the RGO FET based sensors that detect chemical concentrations in the aqueous environment. The RGO FET sensitivity analysis is also demonstrated to provide guidance for RGO FET application and manufacturing.