A Neural Network Approach to the Modeling of Heterojunction Bipolar Transistors from S-Parameter Data

Artificial neural networks have gained attention as a fast, efficient, flexible and accurate tool in the areas of microwave modeling, simulation and optimization. In this paper, a novel neural network approach is proposed for the modeling of Heterojunction Bipolar Transistors (HBT) directly from their S-Parameter data. The neural network structure incorporates bias current and bias voltage as inputs. This enables us to use the same neural model under different bias conditions. The proposed technique provides reliable neural transistor models, while significantly reducing the cost effort and complexity involved in the modeling of HBT.