A neural architecture for the parameter extraction of high frequency devices [MMICs]

Author

Avitabile, G. ; Chellini, B. ; Fedi, G. ; Luchetta, A. ; Manetti, S.

Author_Institution

Dipt. di Ingegneria Elettrica, Bari Univ., Italy

Volume

3

fYear

2001

fDate

6-9 May 2001

Firstpage

577

Abstract

A novel optimization technique for the parameter identification of microwave monolithic integrated circuits is presented. It is based on a hybrid neural network whose learning process convergence allows the validation of the circuit approximated lumped model. The main feature of such a learning process is that no external desired signal is required and the neural network can be considered of the unsupervised type. Furthermore, the neural network output represents the lumped circuit parameter estimation

Keywords

MMIC; circuit CAD; circuit optimisation; convergence; integrated circuit design; multilayer perceptrons; neural net architecture; parameter estimation; unsupervised learning; HF devices; MMIC parameter identification; circuit approximated lumped model; high frequency devices; hybrid neural network; learning process convergence; lumped circuit parameter estimation; microwave monolithic integrated circuits; neural architecture; optimization technique; parameter extraction; unsupervised type; Artificial neural networks; Circuit testing; Frequency; Microwave devices; Microwave theory and techniques; Monolithic integrated circuits; Neural networks; Parameter estimation; Parameter extraction; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-6685-9

Type

conf

DOI

10.1109/ISCAS.2001.921376

Filename

921376