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

TL;DR

This paper introduces a simple, explicit method for extracting small-signal parameters of single-layer graphene FETs, validated by high-frequency measurements, enabling improved RF circuit design.

Contribution

It presents the first straightforward extraction procedure that accounts for frequency and gate voltage dependence, including removal of contact and gate resistances.

Findings

Validated small-signal parameters up to 18 GHz.

Demonstrated non-reciprocal capacitance model validation.

Provided models for frequency-dependent gain and oscillation frequencies.

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.