n this paper we report the development of an artificial neural network to extract a 17-element small-signal circuit model of high electron mobility transistors (HEMTs) and one associated noise temperature value. By this procedure, we are able to reproduce the small-signal and noise performance of several device types from only one measured scattering parameter set, one frequency point and one noise figure value. The employed noise figure is measured in input matched conditions (i.e. 50 Ω source impedance), namely F50. The output noise temperature is associated to the drain-source resistance in the HEMT equivalent circuit according to the noise temperature model by Pospieszalski. The noise parameters of the device under test are then calculated by CAD simulation of the circuit and compared with measurement results. The trained network outputs were used by means of a commercial CAD tool, to simulate and fit measurements performed down to cryogenic temperatures with very good agreement. We observed that the difference that occurs between the expected value of the noise temperature and the average value calculated by the neural network leads to negligible variations in the behavior of the simulated noise parameters.

Cryogenic HEMT noise modeling by artificial neural networks

CADDEMI, Alina;DONATO, Nicola
2005

Abstract

n this paper we report the development of an artificial neural network to extract a 17-element small-signal circuit model of high electron mobility transistors (HEMTs) and one associated noise temperature value. By this procedure, we are able to reproduce the small-signal and noise performance of several device types from only one measured scattering parameter set, one frequency point and one noise figure value. The employed noise figure is measured in input matched conditions (i.e. 50 Ω source impedance), namely F50. The output noise temperature is associated to the drain-source resistance in the HEMT equivalent circuit according to the noise temperature model by Pospieszalski. The noise parameters of the device under test are then calculated by CAD simulation of the circuit and compared with measurement results. The trained network outputs were used by means of a commercial CAD tool, to simulate and fit measurements performed down to cryogenic temperatures with very good agreement. We observed that the difference that occurs between the expected value of the noise temperature and the average value calculated by the neural network leads to negligible variations in the behavior of the simulated noise parameters.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/1433248
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