Straightforward Bias and Frequency Dependent Small-Signal Model Extraction for Single-Layer Graphene FETs

Abstract

We propose an explicit small-signal graphene field-effect transistor (GFET) parameter extraction procedure based on a charge-based quasi-static model. The dependence of the small-signal parameters on both gate voltage and frequency is precisely validated by high-frequency (up to 18 GHz) on-wafer measurements from a 300 nm device. These parameters are studied simultaneously, in contrast to other works which focus exclusively on few. Efficient procedures have been applied to GFETs for the first time to remove contact and gate resistances from the Y-parameters. The use of these methods yields straightforward equations for extracting the small-signal model parameters, which is extremely useful for radio-frequency circuit design. Furthermore, we show for the first time experimental validation vs. both gate voltage and frequency of the intrinsic GFET non-reciprocal capacitance model. Accurate models are also presented for the gate voltage-dependence of the measured unity-gain and maximum oscillation frequencies as well as of the current and power gains.

References in corpus (7)

• A Comprehensive Graphene FET Model for Circuit Design
• Large-Signal Model of Graphene Field-Effect Transistors -- Part I: Compact Modeling of GFET Intrinsic Capacitances
• Small-signal model for 2D-material based field-effect transistors targeting radio-frequency applications: the importance of considering non-reciprocal capacitances
• A small-signal GFET equivalent circuit considering an explicit contribution of contact resistances
• Low-frequency noise parameter extraction method for single layer graphene FETs
• Non-quasi-static effects in graphene field-effect transistors under high-frequency operation
• Small-signal parameters extraction and noise analysis of CNTFETs

Cited by in corpus (1)

• Exploiting ambipolarity in graphene field-effect transistors for novel designs on high-frequency analog electronics