Neuro-space mapping for modeling heterojunction bipolar transistor

Abstract

A neuro-space mapping (Neuro-SM) for modeling heterojunction bipolar transistor (HBT) is presented, which can automatically modify the input signals of the given model by neural network. The novel Neuro-SM formulations for DC and small-signal simulation are proposed to obtain the mapping network. Simulation results show that the errors between Neuro-SM models and the accurate data are less than 1%, demonstrating that the accurcy of the proposed method is higher than those of the existing models.

Keywords

heterojunction bipolar transistor (HBT) / nonlinear device modeling / neural network / neuro-space mapping / optimization

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