Electrolyte-Gated Graphene Field-Effect Transistors: Modeling

This work presents a model for electrolyte-gated graphene field-effect transistors (EGFETs) that incorporates the effects of the double layer capacitance and the quantum capacitance of graphene. The model is validated through experimental graphene EGFETs, which were fabricated and measured to provide experimental data and extract graphene EGFET parameters such as mobility, minimum carrier concentration, interface capacitance, contact resistance, and effective charged impurity concentration. The proposed graphene EGFET model accurately determines a number of properties necessary for circuit design such as currentvoltage characteristics, transconductance, output resistance, and intrinsic gain. The model can also be used to optimize the design of EGFETs. For example, simulated and experimental results show that avoiding the practice of partial channel passivation enhances the transconductance of graphene EGFETs. Graphene EGFETs are fabricated for pH sensing. The location of the Dirac point is measured for pH concentrations varying from 4 to 10. In this range, graphene EGFETs are shown to produce -50.8 mV/pH sensitivity. Graphene EGFETs are also fabricated for use in a real-time polymerase chain reaction (RTPCR) system. RTPCR is run successfully to identify DNA segments thought responsible for the metabolism of clopidogrel, a widely prescribed antiplatelet medication. The graphene EGFETs, however, failed to sense an increase in DNA concentration. Further optimization of the PCR mix is required to ensure that increased DNA concentration lowers the PCR mix pH without rendering the DNA polymerase ineffective. Lastly, graphene EGFETs fabricated for electrogenic cell sensing using the optimized parameters from the newly developed graphene EGFET current-voltage model. Hippocampal mouse neurons were cultured on top of the graphene EGFETs in attempt record action potentials. Thesis Supervisor: Tomis Palacios Title: Professor of Electrical Engineering and Computer Science