Neural-Based Large-Signal Device Models Learning First- Order Derivative Parameters for Intermodulation Distortion Prediction

A detailed procedure to learn a nonlinear model together with its first-order derivative data is presented. Two correlated multilayer perceptron (MLP) neural networks providing the model and its first-order derivatives, respectively, are trained simultaneously. Applying this method to FET devices leads to nonlinear models for current and charge fitting derivative parameters. The training data is the biasdependent equivalent circuit parameters extracted from S-parameter measurements. The resulting models are suitable for both small-signal and large-signal analyses, in particular for intermodulation distortion prediction. Examples for power amplifier simulations of power transfer, efficiency and intermodulation distortion performances are presented.