The aim of the present work is the development of a suitable neural network structure to compute the microwave noise parameters of High Electron Mobility Transistors (HEMT). The noise parameters (NP) here considered are the magnitude (Γopt) and phase (Γopt) of the optimum noise source reflection coefficient, the minimum noise figure (Fmin) and the noise resistance (Rn). By this procedure, we are able to reproduce the above noise parameters of several device types from only one measured scattering parameter set, one frequency point and one generic noise figure value at different bias and/or temperature conditions. The employed noise figure is that measured in input matched conditions (i.e. 50 source impedance), namely F50. The trained network outputs were compared with data of noise measurements performed in our lab and the results have shown to be in a very good agreement. We observed that the difference occurring between the expected value of the noise parameters and the value calculated by the neural network is negligible. The procedure here presented is the most effective choice to avoid the need of any circuit simulations. Our neural network allows great time saving, thus leading to a rapid and complete characterization of the active microwave device under test with a remarkable generalization feature.

Simulating noise performance of advanced devices down to cryogenic temperatures

CADDEMI, Alina;DONATO, Nicola
2005

Abstract

The aim of the present work is the development of a suitable neural network structure to compute the microwave noise parameters of High Electron Mobility Transistors (HEMT). The noise parameters (NP) here considered are the magnitude (Γopt) and phase (Γopt) of the optimum noise source reflection coefficient, the minimum noise figure (Fmin) and the noise resistance (Rn). By this procedure, we are able to reproduce the above noise parameters of several device types from only one measured scattering parameter set, one frequency point and one generic noise figure value at different bias and/or temperature conditions. The employed noise figure is that measured in input matched conditions (i.e. 50 source impedance), namely F50. The trained network outputs were compared with data of noise measurements performed in our lab and the results have shown to be in a very good agreement. We observed that the difference occurring between the expected value of the noise parameters and the value calculated by the neural network is negligible. The procedure here presented is the most effective choice to avoid the need of any circuit simulations. Our neural network allows great time saving, thus leading to a rapid and complete characterization of the active microwave device under test with a remarkable generalization feature.
0735402892
9780735402898
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/1432447
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact