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

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

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

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

Department of Electrical and Computer Engineering

Jamia Millia Islamia
Nazarbayev University

Research output: Contribution to conference › Paper › peer-review

19 Citations (Scopus)

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 language	English
Pages	277-280
Number of pages	4
Publication status	Published - Jul 2019
Event	16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design - EPFL, Lausanne, Switzerland Duration: Jul 15 2019 → Aug 18 2019

Conference

Conference	16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design
Abbreviated title	SMACD
Country/Territory	Switzerland
City	Lausanne
Period	7/15/19 → 8/18/19

Funding

ACKNOWLEDGMENT The work is supported in parts by Media Lab Asia, Ministry of Electronics & IT (MEITY), Government of India under Visvesvaraya Ph.D. Scheme. The work is also supported in parts by ORAU Grant Number 110119FD4515 at Nazarbayev University, Kazakhstan. The authors would also like to acknowledge the DRDO (an enterprise of Ministry of Defense) for providing access to their measurement facility and the measured data from their device.

Funders	Funder number
Ministry of Electronics & IT
Ministry of Coal, Government of India
Oak Ridge Associated Universities	110119FD4515
Media Lab Asia
Nazarbayev University

Keywords

GaN HEMT
Machine Learning
Modeling
NARX architecture

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Modelling and Simulation
Instrumentation

Cite this

A Reliable and Fast ANN Based Behavioral Modeling Approach for GaN HEMT. / Khusro, Ahmad; Hussain, Saddam; Hashmi, Mohammad et al.
2019. 277-280 Paper presented at 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design , Lausanne, Switzerland.

Research output: Contribution to conference › Paper › peer-review

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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.",

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AU - Ansari, Abdul Qayyum

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N2 - 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.

AB - 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.

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KW - Machine Learning

KW - Modeling

KW - NARX architecture

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

Abstract

Conference

Funding

Keywords

ASJC Scopus subject areas

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