LNA characterization methodologies
Summary Joint characterization of low‐noise amplifiers regarding stochastic and deterministic parameters based on output power measurement for a set of known input termination impedances is considered. As an advantage of this method, both noise parameters and the input impedance can be obtained for...
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Veröffentlicht in: | International journal of circuit theory and applications 2017-09, Vol.45 (9), p.1185-1202 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Summary
Joint characterization of low‐noise amplifiers regarding stochastic and deterministic parameters based on output power measurement for a set of known input termination impedances is considered. As an advantage of this method, both noise parameters and the input impedance can be obtained for narrow and broad band applications without special equipment. This method is mathematically developed and practically verified by simulations and measurements. With the knowledge of at least four real‐valued stochastic, and three real‐valued deterministic parameters, the amplifier is characterized, and the noise figure, gain, and input reflection can be calculated as function of the matching network.
Two different ways of processing measured data will be shown.
Advantages of the proposed method compared with established methods are as follows:
No special equipment (e.g., network analyzer, calibrated noise source, noise figure meter, slide screw tuner,…) is needed, just a tunable power meter with known noise bandwidth.
The four real‐valued noise parameters can be obtained with four terminating impedances only.
With three additional terminating impedances, the input impedance of the low‐noise amplifiers can be obtained.
With every additional terminating impedance, the accuracy of the measurement is improved.
Outliers are detected and eliminated.
Copyright © 2016 John Wiley & Sons, Ltd.
LNA Characterization Methodologies: Output power measurement based noise parameter estimation using different terminations. Special algorithm calculates S11 and removes outliers automatically, |
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ISSN: | 0098-9886 1097-007X |
DOI: | 10.1002/cta.2276 |