Abstract
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 ImS12 with mean
square error (MSE) of 1.0449e-05, while LM-BPNN predicts
ImS12 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.
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