A Highly Linear Ka-Band GaN-on-Si Active Balanced Mixer for Radar Applications

Cidronali, Alessandro; Pagnini, Lorenzo; Collodi, Giovanni; Passafiume, Marco

doi:10.1109/tcsi.2022.3193960

Cited by 9 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the changes in channel temperature (T ch ) resulting from fluctuations in drain currents, we conducted experiments to observe the behavior of drain currents under different temperature conditions. Our findings indicated a consistent linear decrease in drain current across various temperature settings [29]. Additionally, we computed power levels (I DS × V DS ) from the output characteristics of the device.…”

Section: Resultssupporting

confidence: 59%

See 1 more Smart Citation

Reliability Assessment of On-Wafer AlGaN/GaN HEMTs: The Impact of Electric Field Stress on the Mean Time to Failure

Chakraborty,

Kim

2023

Micromachines

View full text Add to dashboard Cite

We present the mean time to failure (MTTF) of on-wafer AlGaN/GaN HEMTs under two distinct electric field stress conditions. The channel temperature (Tch) of the devices exhibits variability contingent upon the stress voltage and power dissipation, thereby influencing the long-term reliability of the devices. The accuracy of the channel temperature assumes a pivotal role in MTTF determination, a parameter measured and simulated through TCAD Silvaco device simulation. Under low electric field stress, a gradual degradation of IDSS is noted, accompanied by a negative shift in threshold voltage (ΔVT) and a substantial increase in gate leakage current (IG). Conversely, the high electric field stress condition induces a sudden decrease in IDSS without any observed shift in threshold voltage. For the low and high electric field conditions, MTTF values of 360 h and 160 h, respectively, were determined for on-wafer AlGaN/GaN HEMTs.

show abstract

Section: Resultssupporting

confidence: 59%

“…In the realm of radio frequency (RF) and microwave electronics, AlGaN/GaN HEMTs offer exceptional high-frequency performance, low noise characteristics, and high-power output [24][25][26][27]. These transistors are critical components in radar systems, wireless communication infrastructure, and satellite communications [28,29].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of On-Wafer AlGaN/GaN HEMTs: The Impact of Electric Field Stress on the Mean Time to Failure

Chakraborty,

Kim

2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…The highest level of platform control is actuated by a microcontroller unit, which allows a fast connection to a remote processor unit through which the baseband RX channel signals can be acquired coherently, while the postprocessing of the radar image was carried out using a MATLAB code. Each receiver path is equipped with a single mixer device, as is common in FMCW radar architectures [37], [52]. The mixer is followed by the six-channel Analog Devices AD8283 integrated radar receive path, which is equipped with a corresponding number of analog front end and a 12-bit analog-to-digital converter capable of a maximum sampling frequency of 72 MSPS [53].…”

Section: A Dataset Generation 1) Experiment-based Datasetmentioning

confidence: 99%

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

Rojhani

Passafiume

Sadeghibakhi

et al. 2023

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

Deep neural networks (DNNs) have become a relevant subject in the classification of radio frequency signals and remote sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN training and the effort that making experimental measurements requires. Hence, the quality and quantity of data used for the training and testing of models are crucial for effective classifier development. The training dataset should cover a wide range of cases that synthesize the actual scenarios being classified. This work proposes a novel data augmentation method based on a deterministic model to generate a simulated dataset of radar micro-Doppler signatures suitable for unmanned aerial vehicle (UAV) target classification, without requiring measurement data. It is shown that the DNN trained using the properly generated model-based data offers improved classification accuracy performance. Resultsare presented for a two-class classification of the number of UAV motors using a 77-GHz frequency-modulated continuouswave (FMCW) automotive radar system. The effectiveness of the proposed methodology is proven: a classification accuracy of 78.68% is achieved using a convolutional neural network (CNN) trained using the synthetic dataset, while an accuracy of 66.18% is achieved by using a typical signal processing data augmentation method on a limited measured dataset.

show abstract

“…1,2 These devices exhibit enhancements in breakdown voltage, electron mobility, saturation velocity, parasitic capacitance, and frequency responsiveness. 3 Their versatility has led to their application in a wide range of fields, such as power amplification, 4 RF switching, 5 radar systems, 6 satellite communications, 7 and electric vehicles, 8 underscoring their utility. Given the increasing demand for such applications, the significance of GaN HEMTs is poised to expand further soon.…”

Section: Introductionmentioning

confidence: 99%

Fractional order capacitance behavior due to hysteresis effect of ferroelectric material on GaN HEMT devices

Pyngrope,

Majumdar,

Crupi

2024

Int J Numerical Modelling

View full text Add to dashboard Cite

In recent years, gallium nitride (GaN) high electron mobility transistors (HEMTs) have come to the forefront of the semiconductor industry because of their exceptional performance in both high‐power and high‐frequency utility. Accurate capacitance modeling is crucial to optimize performance and facilitate energy‐efficient electronic circuit design. In order to reflect the complex nature of the aluminum scandium nitride (AlScN) gate capacitance in GaN HEMTs this study investigates the use of the unique Grünwald‐Letnikov model based on fractional order calculus. The proposed model presents a powerful approach to accurately characterize capacitance since fractional order derivatives allow modeling of non‐integer order systems. Quantitative assessment of the Grünwald‐Letnikov model's accuracy is performed through various error metrics, including mean absolute error (MAE), root mean square error (RMSE), maximum percentage error (MPE), mean absolute percentage error (MAPE), and mean squared error (MSE), by comparing the model's predictions to experimental data. Notably, this model demonstrates remarkable consistency in error metrics, with maximum values of MPE = 0.21%, MAE = 0.05%, MAPE = 0.33%, MSE = 0.01%, and RMSE = 0.09% for the forward scan, and MPE = 0.32%, MAE = 0.04%, MAPE = 0.39%, MSE = 0.01%, and RMSE = 0.08% for the backward scan. These metrics affirm the model's precision in capturing the nuanced capacitance characteristics of GaN HEMT devices. Hence, herein for the first time, the novel Grünwald‐Letnikov model, augmented by fractional order calculus, proves to be a robust tool for accurately characterizing GaN HEMT capacitance. Its ability to seamlessly account for the complexities introduced by using ferroelectric material highlights its potential for advancing semiconductor design and optimizing device performance.

show abstract

A Highly Linear Ka-Band GaN-on-Si Active Balanced Mixer for Radar Applications

Cited by 9 publications

References 43 publications

Reliability Assessment of On-Wafer AlGaN/GaN HEMTs: The Impact of Electric Field Stress on the Mean Time to Failure

Reliability Assessment of On-Wafer AlGaN/GaN HEMTs: The Impact of Electric Field Stress on the Mean Time to Failure

Model-Based Data Augmentation Applied to Deep Learning Networks for Classification of Micro-Doppler Signatures Using FMCW Radar

Fractional order capacitance behavior due to hysteresis effect of ferroelectric material on GaN HEMT devices

Contact Info

Product

Resources

About