A novel identification technique for lumped models of general electronic circuits (i.e. MOSFET, BJT, monolithic integrated circuits and filters) is presented. The approach is based on a neural network having a supplementary layer and an adapted learning process, whose convergence allows the validation of the device model. The supplementary layer is another neural network trained offline on the model under exam. The inputs of the network are geometrical parameters and the neural network output represents the lumped circuit parameter estimation.

A double integrated neural network for identification of geometrical features dependency in lumped models / A. Luchetta; S. Manetti; L. Pellegrini. - STAMPA. - 4:(2004), pp. 2895-2899. (Intervento presentato al convegno IEEE 2004 International Joint Conference on Neural Networks tenutosi a Budapest, Hungary nel 25-29 July 2004) [10.1109/IJCNN.2004.1381120].

A double integrated neural network for identification of geometrical features dependency in lumped models

LUCHETTA, ANTONIO;MANETTI, STEFANO;
2004

Abstract

A novel identification technique for lumped models of general electronic circuits (i.e. MOSFET, BJT, monolithic integrated circuits and filters) is presented. The approach is based on a neural network having a supplementary layer and an adapted learning process, whose convergence allows the validation of the device model. The supplementary layer is another neural network trained offline on the model under exam. The inputs of the network are geometrical parameters and the neural network output represents the lumped circuit parameter estimation.
2004
Proceedings of IEEE 2004 International Joint Conference on Neural Networks
IEEE 2004 International Joint Conference on Neural Networks
Budapest, Hungary
25-29 July 2004
A. Luchetta; S. Manetti; L. Pellegrini
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/593797
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