An X-band GaN HEMT power amplifier design using an artificial neural network modeling technique

Lee, Sang-Yun; Cetiner, Bedri A.; Torpi, Hamid; Cai, Shiwei; Jiang, Li; Alt, K. W.; Chen, Y.L.; Wen, Cheng P.; Wang, K.L.; Itoh, T.

doi:10.1109/16.906442

Cited by 31 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In theory, any mathematical relationship can be approximated by a network as long as there are sufficient neurons in the hidden layers. [34][35][36][37][38] This study employs a two-layer structure in order to achieve a balance between model performance and size, the topology is presented in Figure 4.…”

Section: Ann and Ga Initialized Modelsmentioning

confidence: 99%

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Liu,

Cai

2023

Int J Numerical Modelling

View full text Add to dashboard Cite

A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large‐signal behavior, machine learning‐based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning‐based large‐signal modeling techniques is presented in this article. To develop large‐signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm‐assisted ANN (GA‐ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five‐fold cross‐validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed.

show abstract

Section: Ann and Ga Initialized Modelsmentioning

confidence: 99%

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Liu,

Cai

2023

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

Section: Model Development Framework a Ann And Ga Initialized Modelsmentioning

confidence: 99%

“…One hidden layer with sufficient neurons can approximate any mathematical relationship [39]. However, the increase in the number of hidden layers with less neurons in each layer can yield better performance [39]. This is due to the characteristic that the deeper the layer the more information can be fetched by unit neurons in each layer.…”

Section: Model Development Framework a Ann And Ga Initialized Modelsmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

“…Recently, a computer-aided-design (CAD) approach based on neural networks has been introduced in RF and microwave active linear/nonlinear modelling [5][6][7]. The artificial neural network (ANN) modelling techniques are efficient alternatives to conventional methods such as numerical modelling methods, which could be computationally expensive, or analytical methods, which could be difficult to obtain for new devices or empirical models, whose ranges and accuracies could be limited.…”

Section: Introductionmentioning

confidence: 99%

Microwave nonlinear device modelling by using an artificial neural network

Gao

Boeck

2006

Semicond. Sci. Technol.

View full text Add to dashboard Cite

An approach for the microwave nonlinear device modelling technique based on a combination of the conventional equivalent circuit model and artificial neural network (ANN) is presented in this paper. The main advantage of the proposed method is that the integration and differential of an ANN can directly be carried out from the original ANN. The proposed technique is very useful for neural-based microwave computer-aided design, and for analytically unified dc, small signal and nonlinear device modelling. Examples of the Schottky diode and PHEMT linear/nonlinear modelling utilizing the proposed integration and differential technique are demonstrated.

show abstract

An X-band GaN HEMT power amplifier design using an artificial neural network modeling technique

Cited by 31 publications

References 10 publications

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

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

Microwave nonlinear device modelling by using an artificial neural network

Contact Info

Product

Resources

About