In this paper, a virtual test-bench for extracting the 50 Ω noise factor of mismatched devices has been tailored in a CAD environment. Every component of the measurement chain has been experimentally characterized and it has been employed as a sub-circuit. Moreover, the relevant electrical models have been extracted for taking into account the influence of each subcircuit in the chain. Evidence of the simulation effectiveness is provided by comparing the test-bench data with the noise factor calculated from measured scattering and noise parameters in the 6-18 GHz frequency range. The proposed virtual test-bench is not only a valid tool for the challenging task of accurate 50 Ω noise factor measurements of mismatched devices, but also for the sensitivity analysis of each component in the chain, thus evaluating how they affect the accuracy of the final result. Moreover, it can be used for building an automated and customizable measurement chain. Finally, it might be employed as a training tool since every step of the measurement is precisely analyzed and all the steps involved within the 50 Ω noise factor extraction are extensively clarified.

A virtual test-bench for noise figure measurements of mismatched devices

Emanuele Cardillo
;
Alina Caddemi
2018-01-01

Abstract

In this paper, a virtual test-bench for extracting the 50 Ω noise factor of mismatched devices has been tailored in a CAD environment. Every component of the measurement chain has been experimentally characterized and it has been employed as a sub-circuit. Moreover, the relevant electrical models have been extracted for taking into account the influence of each subcircuit in the chain. Evidence of the simulation effectiveness is provided by comparing the test-bench data with the noise factor calculated from measured scattering and noise parameters in the 6-18 GHz frequency range. The proposed virtual test-bench is not only a valid tool for the challenging task of accurate 50 Ω noise factor measurements of mismatched devices, but also for the sensitivity analysis of each component in the chain, thus evaluating how they affect the accuracy of the final result. Moreover, it can be used for building an automated and customizable measurement chain. Finally, it might be employed as a training tool since every step of the measurement is precisely analyzed and all the steps involved within the 50 Ω noise factor extraction are extensively clarified.
2018
9781538624746
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3129900
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