Modeling of heterojunction bipolar transistors based on novel Wiener‐type dynamic neural network

Abstract

A novel Wiener-type dynamic neural network modeling method for heterojunction bipolar transistors (HBTs) is proposed in this article. The model structure of the novel Wiener-type dynamic neural network applicable to HBT modeling is determined by the vector fitting. For the novel Wiener-type dynamic neural network structure, we also proposed a training algorithm applicable to this novel structure to improve its accuracy. We considered the transmission characteristics of the HBT and use the H-parameter to replace the Y-parameter for small-signal analysis. The DC and small-signal model formulas for HBT are also derived. The proposed novel Wiener-type dynamic neural network modeling method does not require detailed parameter information and the internal design structure of the HBT and can model any type of HBT as long as accurate training data are available. In this paper, an application example on modeling a 3 × 30 μ m 2 InGaP/GaAs HBT is presented, and a combined DC and small-signal model is established to demonstrate the accuracy and feasibility of the proposed novel Wiener-type dynamic neural network modeling method. The modeling method can also be used for computer-aided design, and the established models can be more easily applied to circuit simulation.