GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature
Qian Lin, Xiaozheng Wang, Haifeng Wu
Abstract
In this paper, the scattering parameter (S-parameter) modeling method for heterojunction bipolar transistor (HBT) at different temperatures is investigated. The S-parameters of HBT at different temperatures are randomly divided into training and test sets, which are modeled by radial basis function (RBF) neural network and general regression neural network (GRNN), respectively. Then, the fitting results of the two models in predicting S-parameter are displayed. The experimental show that the fitting results of RBF neural network are good, but the fitting errors of some data are existed. Meanwhile most of the data predicted by GRNN can achieve ideal fitting. In addition, the absolute error curves of the two models show that the error curve of RBF neural network is more volatile, while the error curve of GRNN has a small fluctuation range, which can predict the S-parameters more stably. Finally, the mean square error (MSE) of RBF neural network prediction model and GRNN prediction model are 8.6178e-04 and 3.1041e-4, respectively. It is proved that GRNN has a better modeling effect for HBT S-parameter at different temperatures. Therefore, the proposed modeling method can accurately characterize the S-parameter of HBT at different temperatures.