This work shows that physically meaningful wideband, multi-bias small-signal modeling of HBTs can be efficiently and accurately achieved using a genetic algorithm (GA). The physical significance of the equivalent circuit parameters extracted by the GA is checked using a direct extraction technique (DET). For each point we obtained a good agreement between the parameters extracted by the DET and by the GA, which demonstrates the ability of the GA to efficiently extract a physically significant small-signal model.
HBT small-signal model extraction using a genetic algorithm / R., Menozzi; Borgarino, Mattia; J., Tasselli; A., Marty. - STAMPA. - Not available:(1998), pp. 157-160. (Intervento presentato al convegno Gallium Arsenide Integrated Circuit (GaAs IC) Symposium tenutosi a Atlanta GA (USA) nel 1-4 november 1998).
HBT small-signal model extraction using a genetic algorithm
BORGARINO, Mattia;
1998
Abstract
This work shows that physically meaningful wideband, multi-bias small-signal modeling of HBTs can be efficiently and accurately achieved using a genetic algorithm (GA). The physical significance of the equivalent circuit parameters extracted by the GA is checked using a direct extraction technique (DET). For each point we obtained a good agreement between the parameters extracted by the DET and by the GA, which demonstrates the ability of the GA to efficiently extract a physically significant small-signal model.
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