A Neural Architecture for the Parameter Extraction of High Frequency Devices

A novel optimization technique for the parameter identification of microwave monolithic integrated circuit is presented. It is based on a hybrid neural network whose learning process convergence allows the validation of the circuit approximated lumped model. The main feature of such a learning process is that no external desired signal is required and the neural network can be considered of the unsupervised type. Furthermore the neural network output represents the lumped circuit parameter estimation.