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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.

In this article, the bias-dependence of intrinsic channel thermal noise of single-layer graphene field-effect transistors (GFETs) is thoroughly investigated by experimental observations and compact modeling. The findings indicate an increase of the specific noise as drain current increases whereas a saturation trend is observed at very high carrier density regime. Besides, short-channel effects like velocity saturation also result in an increment of noise at higher electric fields. The main goal of this work is to propose a physics-based compact model that accounts for and accurately predicts the above experimental observations in short-channel GFETs. In contrast to long-channel MOSFET based models adopted previously to describe thermal noise in graphene devices without considering the degenerate nature of graphene, in this work a model for short-channel GFETs embracing the 2D materials underlying physics and including a bias dependency is presented. The implemented model is validated with de-embedded high frequency data from two short-channel devices at Quasi-Static region of operation. The model precisely describes the experimental data for a wide range of low to high drain current values without the need of any fitting parameter. Moreover, the consideration of the degenerate nature of graphene reveals a significant decrease of noise in comparison with the non degenerate case and the model accurately captures this behavior. This work can also be of outmost significance from circuit designers aspect, since noise excess factor, a very important figure of merit for RF circuits implementation, is defined and characterized for the first time in graphene transistors.

The trap-induced hysteresis on the performance of a graphene field-effect transistor is experimentally diminished here by applying consecutive gate-to-source voltage pulses of opposing polarity. This measurement scheme is a practical and suitable approach to obtain reproducible device characteristics. Trap-affected and trap-free experimental data enable a discussion regarding the impact of traps on static and dynamic device performance. An analytical drain current model calibrated with the experimental data enables the study of the traps effects on the channel potential within the device. High-frequency figures of merit and the intrinsic gain of the device obtained from both experimental and synthetic data with and without hysteresis show the importance of considering the generally overlooked impact of traps for analog and high-frequency applications.

In this paper, a detailed parameter extraction methodology is proposed for low-frequency noise (LFN) in single layer (SL) graphene transistors (GFETs) based on a recently established compact LFN model. Drain current and LFN of two short channel back-gated GFETs (L=300, 100 nm) were measured at lower and higher drain voltages, for a wide range of gate voltages covering the region away from charge neutrality point (CNP) up to CNP at p-type operation region. Current-voltage (IV) and LFN data were also available from a long channel SL top solution-gated (SG) GFET (L=5 um), for both p- and n-type regions near and away CNP. At each of these regimes, the appropriate IV and LFN parameters can be accurately extracted. Regarding LFN, mobility fluctuation effect is dominant at CNP and from there the Hooge parameter aH can be extracted while the carrier number fluctuation contribution which is responsible for the well-known M-shape bias dependence of output noise divided by squared drain current, also observed in our data, makes possible the extraction of the NT parameter related to the number of traps. In the less possible case of a Lambda-shape trend, NT and aH can be extracted simultaneously from the region near CNP. Away from CNP, contact resistance can have a significant contribution to LFN and from there the relevant parameter SDR^2 is defined. The LFN parameters described above can be estimated from the low drain voltage region of operation where the effect of Velocity Saturation (VS) mechanism is negligible. VS effect results in the reduction of LFN at higher drain voltages and from there the IV parameter hOmega which represents the phonon energy and is related to VS effect can be derived both from drain current and LFN data.

Graphene devices for analog and RF applications are prone to Low Frequency Noise (LFN) due to its upconversion to undesired phase noise at higher frequencies. Such applications demand the use of short channel graphene transistors that operate at high electric fields in order to ensure a high speed. Electric field is inversely proportional to device length and proportional to channel potential so it gets maximized as the drain voltage increases and the transistor length shrinks. Under these conditions though, short channel effects like Velocity Saturation (VS) should be taken into account. Carrier number and mobility fluctuations have been proved to be the main sources that generate LFN in graphene devices. While their contribution to the bias dependence of LFN in long channels has been thoroughly investigated, the way in which VS phenomenon affects LFN in short channel devices under high drain voltage conditions has not been well understood. At low electric field operation, VS effect is negligible since carriers velocity is far away from being saturated. Under these conditions, LFN can be precicely predicted by a recently established physics-based analytical model. The present paper goes a step furher and proposes a new model which deals with the contribution of VS effect on LFN under high electric field conditions. The implemented model is validated with novel experimental data, published for the first time, from CVD grown back-gated single layer graphene transistors operating at gigahertz frequencies. The model accurately captures the reduction of LFN especially near charge neutrality point because of the effect of VS mechanism. Moreover, an analytical expression for the effect of contact resistance on LFN is derived. This contact resistance contribution is experimentally shown to be dominant at higher gate voltages and is accurately described by the proposed model., Comment: Main Manuscript:10 pages, 6 figures

Graphene is ideally suited for optoelectronic applications. It offers absorption at telecom wavelengths, high-frequency operation and CMOS-compatibility. We report optoelectronic mixing up to to 67GHz using a back-gated graphene field effect transistor (GFET). We also present a model to describe the resulting mixed current. These results pave the way for GETs optoelectronic mixers for mm-wave applications, such as telecommunications and RADAR/LIDAR systems.

This paper presents a chemical microwave flexible sensor based on a resonant electromagnetic transducer in micro-strip technology with poly (3,4-ethylenedioxythiophene) polystyrene sulfonate -- multi wall carbon nanotubes (PEDOT:PSS-MWCNTs) as sensitive material for Volatile Organic Compounds (VOCs) detection. The results show a high sensitivity to ethanol and toluene vapors on the S parameters of a passive resonator over a large frequency range. It is equal to -1.59 kHz/ppm and -1.45 kHz/ppm for ethanol and toluene vapors respectively. This kind of sensor can be integrated into real-time multi-sensing platform adaptable for the Internet of Things (IoT).

This study presents the results on the feasibility of a resonant planar chemical capacitive sensor in the microwave frequency range suitable for gas detection and for wireless communications applications. The objective is to develop a low cost ultra-sensitive sensor that can be integrated into a real time multi-sensing platform. The first demonstrators target the detection of harmful gases such as volatile organic compounds (VOCs) to monitor environmental pollution.

A small-signal equivalent circuit of 2D-material based field-effect transistors is presented. Charge conservation and non-reciprocal capacitances have been assumed so the model can be used to make reliable predictions at both device and circuit levels. In this context, explicit and exact analytical expressions of the main radio-frequency figures of merit of these devices are given. Moreover, a direct parameter extraction methodology is provided based on S-parameter measurements. In addition to the intrinsic capacitances, transconductance and output conductance, our approach allows extracting the series combination of drain/source metal contact and access resistances. Accounting for these extrinsic resistances is of upmost importance when dealing with low dimensional field-effect transistors., Comment: 8 pages, 10 figures, 4 tables

We report on microwave operation of top-gated single carbon nanotube transistors. From transmission measurements in the 0.1-1.6 GHz range, we deduce device transconductance gm and gate-nanotube capacitance Cg of micro- and nanometric devices. A large and frequency-independent gm of about 20 microSiemens is observed on short devices, which meets the best dc results. The capacitance per unit gate length of 60 aF/micrometer is typical of top gates on a conventional oxide with a dielectric constant equal to 10. This value is a factor of 3-5 below the nanotube quantum capacitance which, according to recent simulations, favors high transit frequencies . For our smallest devices, we find a large transit frequency equal to 50 GHz with no evidence of saturation in length dependence.