A Reliable and Fast ANN Based Behavioral Modeling Approach for GaN HEMT

Ahmad Khusro, Saddam Hussain, Mohammad Hashmi, Medet Auyenur , Abdul Qayyum Ansari

Research output: Contribution to conferencePaper

Abstract

The paper proposes an accurate, fast and advanced neural network approach to model the small signal behavior of GaN High Electron Mobility Transistor (HEMT). The presented approach makes use of the nonlinear autoregressive series-parallel and parallel architectures to model a 2×200μm device for a broad frequency range of 1GHz – 18GHz. A comparison is drawn between the two architectures based on the training algorithm, accuracy, convergence rate and number of epochs. An excellent agreement is found between the measured S-parameters and the proposed model for the complete broad frequency range. The proposed model can be embedded into computer aided design tool for an accurate and expedited design process of RF/microwave circuits and systems.
Original languageEnglish
Publication statusPublished - Aug 15 2019
Event16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design - EPFL, Lausanne, Switzerland
Duration: Jul 15 2019Aug 18 2019

Conference

Conference16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design
Abbreviated titleSMACD
CountrySwitzerland
CityLausanne
Period7/15/198/18/19

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Khusro, A., Hussain, S., Hashmi, M., Auyenur , M., & Ansari, A. Q. (2019). A Reliable and Fast ANN Based Behavioral Modeling Approach for GaN HEMT. Paper presented at 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design , Lausanne, Switzerland.