Transistor modeling based on LM-BPNN and CG-BPNN for the GaAs pHEMT

Abstract

In order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (CG) algorithm and the Levenberg–Marquardt (LM) algorithm, the CG-BPNN and LM-BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S-parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the CG-BPNN model is better than another S-parameters when predicting ImS₁₂ with mean square error (MSE) of 7.6632e-06, while LM-BPNN model predicts ImS₁₂ with MSE of 2.4672e-06. Meanwhile, the MSE of CG-BPNN model is higher than LM-BPNN model when predicting all the S-parameters. In addition, it shows a smaller fluctuation range for the error curve of LM-BPNN model, which is more stable than the CG-BPNN model. Therefore, the double hidden layer LM-BPNN model is the better choice to characterize the small signal of GaAs pHEMT.