Efficient Harmonic Balance Simulation of Nonlinear Microwave Circuits with Dynamic Neural Models

This paper presents a novel approach aimed at enhancing the speed and accuracy of harmonic balance (HB) simulation of nonlinear circuits represented by dynamic neural network (DNN) models. A set of constraint functions are proposed using the boundary information extracted from the time-domain training data for developing DNN models. Using these constraints, an expanded HB formulation of DNN is presented ensuring the DNN HB solutions to be within its training region. To further enhance the simulation efficiency, we propose a systematic method for setting the initial values of the DNN HB variables taking advantage of the knowledge of frequency-domain training data. The proposed methods are demonstrated through the HB simulations of a power amplifier circuit and DBS subsystem. It is shown that for simulation of DNN-based circuits, the proposed HB method gives more accurate solutions in shorter time than that using conventional HB method