Comparative Study of Surrogate Modeling Methods for Signal Integrity and Microwave Circuit Applications

Abstract

With a short product cycle as we see today, fast and accurate modeling methods are becoming crucial for the development of new generation of electronics devices. Furthermore, increased complexity in circuitry and integration compounds design iteration and the associated, high-dimensional sensitivity analysis and performance optimization studies. Therefore, black-box surrogate models replacing the actual circuitry offer an attractive alternative for more efficient design iteration, optimization, and even direct Monte Carlo analysis. In this article, surrogate models built using nonparametric Gaussian process (GP) are presented. A robust framework based on probabilistic programming is used for training GP models. Other methods, such as partial least-square regression, support vector regression, and polynomial chaos, are used to compare with the performance of GP. Three design applications, a high-speed channel, a millimeter-wave filter, and a low-noise amplifier are used to demonstrate the robustness of the proposed GP-based surrogate models.