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Artificial Neural Network Modeling for Improved On-Wafer Line-Reflect-Match Calibrations

Published

Author(s)

Jeffrey Jargon, Kuldip Gupta

Abstract

We model a load using an artificial neural network (ANN) to improve an on-wafer line-reflect-match (LRM) calibration of a vector network analyzer. The ANN is trained with measurement data obtained from a thru-reflect-line (TRL) calibration. The accuracy of the LRM calibration using the ANN-modeled load compares favorably to a benchmark multiline TRL calibration with an average worst-case scattering parameter error bound of 0.017 over a 40 GHz bandwidth.
Conference Dates
September 24-28, 2001
Conference Location
London, 1, UK
Conference Title
European Microwave Conference

Keywords

artificial neural network, calibration, line-refelct-match, network analyzer

Citation

Jargon, J. and Gupta, K. (2001), Artificial Neural Network Modeling for Improved On-Wafer Line-Reflect-Match Calibrations, European Microwave Conference, London, 1, UK (Accessed June 13, 2024)

Issues

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Created August 31, 2001, Updated October 12, 2021