Inverse Modeling of S‐Parameters for <scp>GaAs pHEMT</scp> Based on <scp>BiLSTM</scp> ‐ <scp>LSTM</scp>

ABSTRACT To further enhance the precision of transistor modeling, the bidirectional long short term memory‐long short term memory (BiLSTM‐LSTM) neural network is utilized for the inverse modeling of the scattering parameters (S‐parameters) for the gallium arsenide pseudomorphic high electron mobility transistor (GaAs pHEMT) here. The modeling results show that the optimal mean square error (MSE) is up to 0.0001, the coefficient of determination ( R 2 ) is 0.9972 with the optimal mean absolute error (MAE) of 0.0049. Therefore, BiLSTM‐LSTM has superior advantage for the nonlinear relationship modeling for the S‐parameters of GaAs pHEMT.