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

• Published in: International journal of RF and microwave computer-aided engineering Vol. 30; no. 4
• Main Authors: Khusro, Ahmad, Husain, Saddam, Hashmi, Mohammad S., Ansari, Abdul Quaiyum
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2020; Hindawi Limited
• Subjects: Accuracy; Algorithms; artificial neural network (ANN); Artificial neural networks; cascade forward neural network; Comparative studies; Convergence; Frequency ranges; GaN HEMT; High electron mobility transistors; Mathematical models; Model accuracy; Model testing; modeling; multilayer perceptron (MLP); Multilayer perceptrons; Neural networks; Parameters; Radio frequency; Semiconductor devices; S‐parameters
• ISSN: 1096-4290; 1099-047X
• DOI: 10.1002/mmce.22112

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.