Comparative analysis of parameter extraction techniques for AlGaN/GaN HEMT on silicon/sapphire substrate. (November 2017)

• Record Type: Journal Article
• Authors: Majumdar, Shubhankar; Bag, Ankush; Biswas, Dhrubes
• Is Part Of: Microelectronics and reliability. Volume 78(2017)
• Page Start: 389
• Page End: 395
• Publication Date: 2017-11
• Subjects: Artificial neural network (ANN) model -- Small signal model (SSM) -- GaN HEMT on silicon/sapphire -- RF characterization; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2017.08.016
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: We report a comparative study of artificial neural network (ANN) model and small signal model (SSM) based on extracted parameters. ANN model training is done using Levenberg-Marquardt back propagation algorithm, whereas SSM is formed by extracting circuit parameters from measured S-parameters of GaN HEMT on Silicon and Sapphire. It has been found that, for the GaN HEMT parameter extraction, it takes 85 hidden layer neurons to produce the output with higher accuracy. The optimized test and training error/performance are found to be 1.12 × 10 − 8 /0.97 and 1 × 10 − 8 /0.99, respectively. Graphical abstract: Highlights: Fabrication and RF characterization of the GaN HEMT on silicon (Si) and sapphire (Al2 O3 ). Implementation of ANN model for determining the small signal model parameter. ANN model utilizes variable weights & same activation function for extraction of SSM parameters of GaN HEMT.