A new and Reliable Decision Tree Based Small-Signal Behavioral Modeling of GaN HEMT

Khusro, Ahmad; Hashmi, Mohammad S.; Ansari, Abdul Quaiyum; Auyenur, Medet

doi:10.1109/mwscas.2019.8885334

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…Khusro et al 10 presented a small‐signal modeling strategy based on SVR for GaN‐HEMT device with multi biasing sections and S ‐parameters by utilizing nonlinear Gaussian or Radial Basis kernel function. Khusro et al 11 explored decision tree based multivariable techniques for small‐signal modeling of a GaN‐HEMT. They utilize Bayesian algorithms to find optimal hyperparameters for higher accuracy.…”

Section: Literature Overviewmentioning

confidence: 99%

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Bhargava

Vadalà

Majumdar

et al. 2022

Int J RF Mic Comp-Aid Eng

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In this article, a study of performing machine learning (ML) based modeling for semiconductor devices has been developed using experimental microwave data. Characterization of gallium arsenide (GaAs) pseudomorphic high electron mobility transistors (pHEMTs) with different gate widths is used as the illustrative example to demonstrate the accuracy and effectiveness of the presented modeling procedure. The tested devices are based on the multifinger layout, in which the total gate width (W) is obtained by multiplying the number of fingers (N f ) and their length (W 0 ). Machines are trained with scattering (S-)parameter measurements up to 65 GHz by using the extreme gradient boosting (XGBoost) algorithm with K-fold cross-validation. Then, the output of the trained machine is utilized by the parameters such as N f and W 0 inside the Auto-encoder (AE) model. In particular, the ML model with AE has a maximum of 99.88% prediction accuracy, despite the uncertainty inherent in the microwave measurements and the unavoidable deviations from the ideal behavior of the analyzed devices.

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Section: Literature Overviewmentioning

confidence: 99%

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Bhargava

Vadalà

Majumdar

et al. 2022

Int J RF Mic Comp-Aid Eng

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“…Suppose the training set, = { , }; where is the multivariate set of inputs composed of training observations of gate and drain bias voltages and is the drain or gate current. By using the standard results, the conditional distribution ( * | , ) is computed using ( 27) and (28).…”

Section: Appendix Bmentioning

confidence: 99%

Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

Jarndal

Husain

Hashmi

et al. 2021

IEEE J. Electron Devices Soc.

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This paper presents an extensive study and demonstration of efficient electrothermal largesignal GaN HEMT modeling approaches based on combined techniques of Genetic Algorithm (GA) with Artificial Neural Networks (ANN), and Particle Swarm optimization (PSO) with Support Vector Regression (SVR). Another promising Gaussian Process Regression (GPR) based large-signal modeling approach is also explored and presented. The GA-ANN addresses the typical problem of local minima associated with the backpropagation (BP) based ANN. The GA successfully aids in the determination of optimal initial values for BP-ANN and enables it to find a unique optimal solution after subsequent of iterations with higher rate of convergence. This is also achieved using PSO-SVR with lower optimization variables. The developed modeling techniques are demonstrated and used to simulate the gate and drain currents of a 2-mm GaN device. All the models are relatively simple, practical, and easy to implement. The gate and drain currents models are embedded in an equivalent large-signal circuit's model and built in Advanced Design System (ADS) software. The implemented model is validated by large-signal measurements and very good fitting results have been obtained. The model also showed an accurate simulation for a nonlinear power amplifier with very good computational speed and convergence.

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“…furthermore, selection of suitable topology of ECMs, physically inconsistent values for complex topologies are major concerns for the designers [9], [10], [23]. Therefore, the alternative machine learning (ML) based techniques to develop SSMs are getting traction [24]- [28], [30]- [32], [34], [35]. These techniques have shown promise as they can emulate complex behaviors, manifest better prediction capability and generally are computationally efficient, nevertheless, requisite large sample size of the measurements [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Investigation and Comparative Analysis of Machine Learning-Based Small-Signal Modelling Techniques for GaN HEMTs

Husain

Hashmi

Ghannouchi

2022

IEEE J. Electron Devices Soc.

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A number of machine learning (ML) algorithm based small signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature. However, these techniques rarely provide any inkling about their suitability in modeling GaN HEMTs under varied operating conditions. In this context, this paper thoroughly investigates various ML based techniques and identifies their suitability for specific application scenarios. At first, an array of commonly employed modeling techniques based around Artificial Neural Network, RANdom SAmple Consensus, Support Vector Regression, Gaussian Process Regression, Decision Tree, and Genetic algorithm assisted Artificial Neural Network are used for development of modeling framework to exploit the bias, frequency and geometry dependence on S-parameter based outputs. Subsequently, the ensemble techniques namely Bootstrap aggregating, Random Forests, Extremely Randomized Trees, AdaBoost, Gradient Tree Boosting, Histogram-based Gradient Boosting, and Extreme Gradient Boosting are also explored to understand the capability of these algorithms in the development of GaN HEMT small signal models. Thereafter, an exhaustive analysis of bias and variance is carried out to figure out the most appropriate algorithms for specific applications. The discrepancies during model development are removed by tuning the hyperparameters of the respective models using Random search optimization with 5-fold cross validation technique. Post tuning, the models are evaluated in terms of generalization capability, Advanced Design System compatibility, computational efficiency, training and simulation time, models' capacity and parameters'tuning time.

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A new and Reliable Decision Tree Based Small-Signal Behavioral Modeling of GaN HEMT

Cited by 18 publications

References 11 publications

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Auto‐encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

Comprehensive Investigation and Comparative Analysis of Machine Learning-Based Small-Signal Modelling Techniques for GaN HEMTs

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