Transistor Modeling Based on LM-BPNN and CG-BPNN for the GaAs pHEMT

In order to address the challenges of complex process and low precision in traditional device modeling, based on the double hidden layer conjugate gradient back propagation neural network (CG-BPNN) and the double hidden layer Levenberg-Marquardt back propagation neural network (LM-BPNN), two small signal models are proposed and analyzed for the gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) 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 is better than another S-parameters when predicting ImS with mean square error (MSE) of 1.0449e-05, while LM-BPNN predicts ImS with MSE of 3.0954e-06. Meanwhile, the MSE of CG-BPNN is higher than LM-BPNN when predicting all the S-parameters. In addition, it shows a smaller fluctuation range for the error curve of LM-BPNN, which is more stable than the CG-BPNN. Therefore, the double hidden layer LM-BPNN is the better choice to model the small signal of GaAs pHEMT.