An Efficient and General Automated Power Amplifier Design Method Based on Surrogate Model Assisted Hybrid Optimization Technique
Bo Liu, Liyuan Xue, Haijun Fan, Yuan Ding, Muhammad Ali Imran, Tao Wu
Abstract
In layout-level optimization-oriented power amplifier (PA) design, the need for a good quality initial design and the high computational cost of electromagnetic (EM) simulations are remaining challenges. To address these challenges, a new method called efficient and general Bayesian neural network (BNN)-assisted hybrid optimization algorithm for PA design (E-GASPAD), is proposed. The key innovations of E-GASPAD include the introduction of BNN to model the PA design landscape and a new hybrid optimization algorithm co-working with BNN prediction for efficient PA design optimization. The performance of E-GASPAD is demonstrated by a 27–31 GHz class-AB PA and a 24–31 GHz wideband Doherty PA. Considering around 30 design variables with wide search ranges, the complete set of PA performance specifications, and full-wave EM simulations, layout-level high-performance designs are obtained automatically within a few hundred simulations (i.e., less than 72 h).