An Efficient and General Automated Power Amplifier Design Method Based on Surrogate Model Assisted Hybrid Optimization Technique

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).