A Dynamic Concentration-Dependent Analytical \textit\, - \textit Model for LG-GFET Biosensor

• Published in: IEEE transactions on electron devices 2023-06, p.1-8
• Main Authors: Wu, Yunqiu, Xu, Tao, Jiang, Ke, Lv, Duoduo, Shi, Ying, Tang, Hong, Zhao, Chenxi, Yu, Yiming, Liu, Huihua, Xu, Yuehang, Kang, Kai
• Format: Article
• Language: English
• Subjects: Biological system modeling; Biosensor; Biosensors; Graphene; Ions; liquid-gated graphene field-effect transistor (LG-GFET); Logic gates; Scattering; Semiconductor process modeling; transistor model
• ISSN: 0018-9383
• DOI: 10.1109/TED.2023.3268139

In the past few years, liquid-gated graphene field-effect transistors (LG-GFETs) have been widely used in biological detection due to their unique advantages. An accurate transistor model is the basis of biological detection circuit design, however, the reported GFET models are mainly focusing on solid-gated GFETs. Therefore, it is essential to conduct the research on LG-GFET model. In this article, an improved \textit{I} - \textit{V} model of LG-GFET is presented based on Fregonese's model. An improved electric double-layer capacitor model is proposed for LG-GFET. Then, the relationship among iron concentration, bias voltages, and current is studied comprehensively. Furthermore, the drain current response change with time is taken into account and the dynamic concentration-dependent model is established. To verify the accuracy of the proposed model, LG-GFET is simulated in TCAD software and fabricated to perform the measurement. The simulation results and measurement results are compared with the model results, respectively. These results show that the relative root-mean-square error (RMSE) to both simulation and measurement results is less than 5.7%. It is revealed that the proposed model can be applied to biological detection and achieve high accuracy.