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.

中文翻译：

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还原氧化石墨烯场效应晶体管的混合建模和灵敏度分析


还原氧化石墨烯（RGO）场效应晶体管（FET）已被开发并应用于各个领域。然而，由于RGO中带隙和态密度（DOS）的随机性，RGO FET的有效建模和灵敏度分析仍然是一个非常具有挑战性的问题。在本文中，我们建议通过整合数据驱动思维和石墨烯 FET 模型来开发混合模型来解决 RGO FET 建模问题。该模型利用石墨烯和 RGO 之间的相似性来推广现有的石墨烯 FET 模型，并采用 RGO FET 漏极电流数据来表征模型的特异性。该模型的基本思想是修改石墨烯DOS以近似RGO DOS，从而可以通过近似RGO DOS来获得电荷密度、迁移率和其他参数。我们使用基于 RGO FET 的传感器验证模型的准确性，该传感器检测水环境中的化学浓度。 RGO FET 灵敏度分析还被证明可为 RGO FET 应用和制造提供指导。