Large-signal modeling methodology for GaN HEMTs for RF switching-mode power amplifiers design

Jarndal, Anwar; Aflaki, Pouya; Negra, Renato; Kouki, Ammar B.; Ghannouchi, Fadhel M.

doi:10.1002/mmce.20485

Cited by 23 publications

(40 citation statements)

References 17 publications

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“…1, which includes both extrinsic and intrinsic elements. The same developed extraction method for the extrinsic and intrinsic elements of the model in [11] has been used. Table 1 lists the extracted extrinsic parameters of the considered 4-W packaged GaN HEMT.…”

Section: Large-signal Modelmentioning

confidence: 99%

“…Comparison of the model simulation with DC and RF small-/large-signal measurements of packaged GaN HEMT is presented in Section 5. In Section 6 the model is demonstrated by simulating the reported inverse class-F power amplifier in [11] based on the same considered packaged GaN HEMT on Si substrate. Finally, a conclusion is drawn in Section 7.…”

Section: Introductionmentioning

confidence: 99%

“…In the past few years, many modeling techniques for GaN HEMT devices have been developed [9][10][11][12][13][14][15][16]. Most of these models are based on table-based, analytical or artificial neural networks (ANN) modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects

Jarndal

Ghannouchi

2016

Solid-State Electronics

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Section: Large-signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects

Jarndal

Ghannouchi

2016

Solid-State Electronics

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“…Tables Table 2.1: Material Characteristics of Si, GaAs, and GaN [3]. 4 Table 2.2: Comparison of Johnson and Baliga figures of merit for semiconductor materials [4]. 8 Table 3.1: Maximum achievable efficiency and increase in efficiency for incrementally increased output harmonic control [12].…”

mentioning

confidence: 99%

“…4: Changes in drain current from the initial values to the final values for each experimental test run. The stressed amplifiers (1,2,6,8) show significantly greater change throughout the test than non-stressed amplifiers (3,4,5,7). 91 ix 78 Figure 5.27: Test fixture 1, stress 1 data.…”

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confidence: 99%

RF Reliability Comparison between DC Stressed and Non-DC-Stressed GaN-on-Si HEMTs in a 1GHz Class F Power Amplifier

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Gallium Nitride (GaN) is a wide band gap semiconductor with an energy gap of 3.4eV. The large energy gap of this III-V semiconductor gives it the ability to withstand high electric fields, high operating temperatures, and allows for high power density. These properties of GaN maintain their advantage at high frequencies and are ideal for use in power amplifiers.This thesis discusses the reliability of a GaN high electron mobility transistor (HEMT) used in a class F power amplifier configuration. GaN HEMTs have only been commercially available since 2006 and as a result their long term reliability is still under investigation. The mechanisms that cause device failures and reduced performance are not fully understood and current work continues to produce new theories as to why they occur and how they affect device performance. The mechanisms that cause changes in device performance have been heavily explored in DC studies. These are useful in understanding the mechanisms that may lead to a change in device performance. However, there is less literature investigating how these mechanisms affect the radio frequency (RF) performance of devices.Using the information gathered from literature about DC studies this thesis launches an investigation to observe the RF behavior of GaN HEMT amplifiers that have been subjected to DC stressing. The DC stressing consists of applying a large negative voltage to the gate terminal while connecting the drain and source terminals to ground. The applied voltage is large enough to cause permanent damage to the device. To perform the investigation four GaN class F power amplifiers are subjected to DC stressing while another four GaN class F power amplifiers receive no DC stressing. The performance of the two groups of amplifiers is compared and evaluated.The eight power amplifiers are constructed to output 4 watts of power. The output power and DC bias are continuously monitored for each test. After a DC stress test, consisting of -70V applied between the gate and drain/source terminals, four observations are noted. One, the drain current, and two, the output powers are initially reduced and recover after roughly a 3 hour period of time. Three, when operated for periods of time greatly exceeding the recovery period there are no differences seen in behavior between stressed and non-stressed amplifiers. These three observations happened in all the test runs. Four, a permanent gate leakage current is observed in all stressed amplifiers.v ACKNOWLEDGEMENTS

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Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs

Jarndal,

Ansari,

Dautov

et al. 2024

Advcd Theory and Sims

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This study presents a novel approach to implementing an artificial neural network (ANN) model for simulating high electron mobility transistors (HEMTs) in Keysight ADS through integrating Verilog‐A coding. It streamlines the realization of ANN models characterized by diverse complexities and layer structures. The proposed method is demonstrated by developing nonlinear models for GaN HEMT on two distinct substrates. GaN‐on‐Si and GaN‐on‐SiC with respective and gate widths are characterized by S‐parameters at a grid of gate and drain bias conditions. The intrinsic gate capacitance and conductances are extracted from the de‐embedded S‐parameters, which are then integrated to find the gate charges and currents. The drain current with the inherent self‐heating and trapping effects is modeled based on the pulsed IV measurement at well‐defined quiescent voltages. Subsequently, the related ANN models of these nonlinear elements are interconnected to form the intrinsic part of the large‐signal model. This intrinsic part with all ANN sub‐models is then completely implemented using a Verilog‐A‐based code. The whole ANN large‐signal model is then validated by single‐ and two‐tone radio frequency large‐signal measurements, which shows a perfect fitting with a high convergence rate. The overall simulation time is five times reduced when the developed Verilog‐A‐based ANN is used instead of the table‐based model. Overall, the large‐signal Verilog‐A‐based ANN model exhibits an improved performance enhancement compared to the conventional table‐based models. This indicates the practical viability of the Verilog‐A integration technique in modeling the nonlinear GaN HEMTs.

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Large-signal modeling methodology for GaN HEMTs for RF switching-mode power amplifiers design

Cited by 23 publications

References 17 publications

Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects

Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects

RF Reliability Comparison between DC Stressed and Non-DC-Stressed GaN-on-Si HEMTs in a 1GHz Class F Power Amplifier

Improved Verilog‐A Based Artificial Neural Network Modeling Applied to GaN HEMTs

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