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Applications of Artificial Neural Networks to RF and Microwave Measurements

Published

Author(s)

Jeffrey Jargon, Kuldip Gupta, Donald C. DeGroot

Abstract

This article describes how artificial neural networks (ANNs) can be used to benefit a number of RF and microwave measurement areas including vector network analyzer (VNA) calibrations, power, and material characterization. We apply ANNs to model a variety of on-wafer and coaxial VNA calibrations, including open-short-load-thru (OSTL) and thru-reflect-line (LRM), and assess the accuracy of the calibrations using these ANN-modeled standards. We find that ANN models compare favorably to benchmark calibrations throughout the frequencies they were trained for. We summarize other current applications of ANNs, including the determination of permittivity of liquids from reflection coefficient measurements. We also discuss some potential applications of ANN models related to power measurements, material characterization, and the comparison of nonlinear vector network analyzers.
Citation
International Journal of Rf and Microwave Computer-Aided Engineering
Volume
12
Issue
1

Keywords

artificial neural network, calibration, measurement, microwave, network analyzer

Citation

Jargon, J. , Gupta, K. and DeGroot, D. (2002), Applications of Artificial Neural Networks to RF and Microwave Measurements, International Journal of Rf and Microwave Computer-Aided Engineering (Accessed April 17, 2024)
Created December 31, 2001, Updated October 12, 2021