Bayesian Inference-Based Behavioral Modeling Technique for GaN HEMTs

Cai, Jialin; King, Justin; Su, Jiangtao; Yu, Chao; Chen, Shichang; Sun, Lingling; Wang, Haiqi; Liu, Jun

doi:10.1109/tmtt.2019.2906304

Cited by 34 publications

(30 citation statements)

References 27 publications

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“…These conventional methods, although accurate, are often found to be highly cumbersome and computationally inefficient. Therefore, the alternative machine learning (ML) based small-signal modeling technique is gaining popularity as their turn-around time is fast with very good accuracy [22]- [23]. A key feature of ML is its ability to predict the outcome in real-time very quickly and this is very appealing for device modeling especially at RF and microwave frequencies where the inter-dependence of various device parameters on each other is huge [24]- [25].…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been used in the development of small-signal model for GaN HEMT [27]- [34]. However, SVR is considered better than ANN as it enables the determination of global optimum which is superior to ANN that supports local optimum [23,25,[32][33][34][35][36][37]. Moreover, the SVR possesses geometrical interpretation whereas ANN is based on the tedious description of various parameters.…”

Section: Introductionmentioning

confidence: 99%

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A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

et al. 2020

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The work reported in this paper explores a novel Particle Swarm Optimization (PSO) tuned Support Vector Regression (SVR) based technique to develop the small-signal behavioral model for GaN High Electron Mobility Transistor (HEMT). The proposed technique investigates issues such as kernel selection and model optimization usually encountered in the application of SVR to model the GaN based HEMT devices. Here, the PSO algorithm is utilized to find the optimal hyperparameters to minimize the fitness function. To enumerate the efficiency and the generalization capability of the predictors, the performance of the model is investigated in terms of mean square error (MSE) and mean relative error (MRE). A very good agreement is found between the measured S-parameters and the proposed model for multi-biasing sets over the complete frequency range of 1 GHz-18 GHz. The proposed technique is even used to test the frequency extrapolation capability of the model. A comparative analysis indicates that the proposed PSO-SVR predictor achieves significantly improved computational efficiency and the overall prediction accuracy. To demonstrate the ready usefulness of the modeling approach, the developed model has been incorporated in CAD environment using MATLAB Cosimulation in ADS Ptolemy. Subsequently, the small-signal stability analysis is performed and gain of a power amplifier configuration designed using the proposed GaN HEMT model is determined.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

et al. 2020

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“…However, in all cases, the neural network size needed to develop an accurate model is not known a priori [24]. In [19]- [21], more advanced machine learning (ML) techniques are applied to device modeling, using Bayesian inference [19]- [20] and support vector regression [21] in order to capture as wide a modeling space as possible i.e. to ensure the models remain accurate across a wide range of power levels and/or frequencies, using a minimal number of measurements.…”

Section: Introductionmentioning

confidence: 99%

A Modified Poly-Harmonic Distortion Model Based on the Canonical Piecewise Linear Functions for GaN HEMTs

Cai

Pan

et al. 2020

IEEE Access

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A novel, large-signal behavioral modeling methodology for Gallium Nitride (GaN) highelectron-mobility transistors (HEMTs), based on the canonical piecewise-linear (CPL) functions, is presented in this paper. The proposed new model employs the poly-harmonic distortion (PHD) model framework, making use of the CPL functions for interpolation of the amplitude of the dominant input signal. The CPL method is also applied to the quadratic PHD (QPHD) model framework, allowing for application to devices operating under high (greater than 3 dB) compression levels. Compared with the standard PHD/QPHD models, which require lengthy tables of parameter values to account for the varying large signal input power(s), the models described in this paper are able to predict transistor behavior at different levels of input power, from the linear region to the strongly nonlinear region (where gain compression exceeds 1 dB), with one single set of model coefficients. The basic theory of the proposed model for both RF and dc responses is provided in the paper. The proposed modeling technique is validated through simulated and experimental data from separate 6 W and 10 W GaN HEMT devices, over a wide range of load conditions and power levels. In addition, a two-dimensional polynomial-based model is used for performance comparison, with the proposed method providing comparable accuracy while requiring significantly fewer model coefficients.

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“…The machine learning technique aids in less time consumption by avoiding numerous calculations of equations, study of device physics and therefore expedites the design optimization process and thereby enhancing the production yield. An increase in the published literature is itself an evidence of the significance of machine learning techniques in device modeling . artificial neural network (ANN), one of the learning techniques, has been emerged as a powerful tool for device behavioral characterization and modeling and accommodates all the features of the machine learning .…”

Section: Introductionmentioning

confidence: 99%

“…An increase in the published literature is itself an evidence of the significance of machine learning techniques in device modeling. [30][31][32][33] artificial neural network (ANN), one of the learning techniques, has been emerged as a powerful tool for device behavioral characterization and modeling and accommodates all the features of the machine learning. [34][35][36][37][38][39][40][41][42][43][44] The advantage of ANN is that it can model highly nonlinear complex relations without even requiring explicit mathematical representations.…”

Section: Introductionmentioning

confidence: 99%

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

Khusro

Husain

Hashmi

et al. 2019

Int J RF Microw Comput Aided Eng

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This article reports a comparative study of two artificial neural network structures and associated variants used to describe and predict the behavior of 2 × 200 μm2 GaN high electron mobility transistors (HEMTs), utilizing radiofrequency characterization. Two architectures namely multilayer perceptron and cascade feedforward, have been investigated in this work to develop the behavioral model. A study is conducted utilizing the two architectures, all trained using Levenberg‐Marquardt, in terms of accuracy, convergence rate, and generalization capability to develop the behavioral model of GaN HEMT. However, to ensure the robustness of the model, accuracy, convergence rate, time elapsed, and generalization capability of the proposed model is also tested under couple of training algorithms, activation functions, number of hidden layers and neuron embedded inside it, methods for initialization of weights and bias and certain other vital parameters playing vital role in influencing the model accuracy and effectiveness. An excellent agreement found between measured S‐parameters and the proposed model proves the effectiveness of the proposed approach and excellent prediction ability for a sweeping multibias set and broad frequency range of 1 to 18 GHz. Moreover, a very good generalization capability is also recorded under variation of crucial parameters of GaN HEMT‐based neural model.

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Bayesian Inference-Based Behavioral Modeling Technique for GaN HEMTs

Cited by 34 publications

References 27 publications

A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

A Modified Poly-Harmonic Distortion Model Based on the Canonical Piecewise Linear Functions for GaN HEMTs

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

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