High-Dimensional Global Optimization Method for High-Frequency Electronic Design

Abstract

Efficient global optimization of microwave systems is a very challenging task that emerges in importance for rapid design closure and discovery of novel structures. As the operating frequency increases, additional difficulties in design optimization occur due to increased nonlinearity, creating a high-dimensional nonconvex response surface. Bayesian optimization (BO) is a promising solution to solve such problems. However, BO-based methods suffer from the curse of dimensionality, where the number of simulations required for convergence increases exponentially with the number of parameters. In this paper, we address this problem and propose a new BO-based high-dimensional global optimization method titled, Bayesian Optimization with Deep Partioning Tree (DPT-BO). DPT-BO leverages a novel DPT that allows for rapid coverage of high-dimensional sample spaces and utilizes an additive Gaussian process (ADD-GP) with a fully additive decomposition, making it more suitable for high-frequency design optimization. We apply DPT-BO to different optimization test functions along with three high-frequency design applications, namely, maximizing signal integrity in high-speed channels, minimizing losses of substrate integrated waveguides with air cavity, and maximizing efficiency of wireless power transfer systems. The results show that DPT-BO finds control parameters that provide better performance in less CPU time compared to other techniques.