Flexible Automatic Design of GaN PA Based on Gaussian Process-Assisted Non-Dominated Sorting Genetic Algorithm

This paper introduces a new method for designing power amplifiers (PAs) using a flexible, automatic approach. It employs a Gaussian process-assisted fast non-dominated sorting genetic algorithm (GP-NSGA-II) for both circuit synthesis and layout optimization. Traditional genetic algorithms are computationally intensive and have poor convergence, making them difficult to use with three-dimensional full-wave simulators. To address these issues, the authors incorporate Gaussian process regression from Bayesian optimization into NSGA-II, improving convergence and computational efficiency. The method integrates layout simulation and optimization, enabling automatic PA design with direct layout optimization. To demonstrate the method’s effectiveness, the authors design a wide-band PA and a tri-band PA using a 10-W gallium-nitride (GaN) high electron mobility transistor (HEMT). The tests show that the prototypes perform well: the wide-band PA has a power-added efficiency (PAE) of over 61% and an output power greater than 41.5 dBm in the 2-3 GHz band, while the tri-band PA achieves PAE values of over 59%, 55%, and 57% in the 0.7-1.1 GHz, 2.3-2.5 GHz, and 3.4-3.5 GHz bands, respectively, with output powers exceeding 41.2 dBm, 40.6 dBm, and 40.6 dBm.