Microwave device design increasingly relies on surrogate modeling to accelerate optimization and reduce costly electromagnetic (EM) simulations. This paper presents a spectral Bayesian optimization (SBO) framework leveraging a physicsinformed Gaussian process (GP) with a rational complex-valued Szegö kernel and input warping to enhance surrogate accuracy and data efficiency. Unlike conventional methods that model scalar objectives, our approach directly learns the complex-valued frequency response, enforcing causality and Hermitian symmetry. Effectiveness is demonstrated in two cases: a zig-zag microstrip bandpass filter optimized for magnitude response, and a passive
differential equalizer optimized for both transmission magnitude and group delay. By embedding prior physics and modeling directly in the frequency domain, the method enables accurate, sample-efficient optimization of frequency-dependent behavior. This work shows how physics-informed Bayesian optimization can significantly improve microwave device design efficiency.
Bridging the AC Non-Equilibrium Green’s Function Formalism and Transmission Line Models for the Analysis of Nanointerconnects
The unfavorable scaling of Cu interconnects at nanoscale dimensions has prompted the search for alternative materials. To model electron transport in these novel nanointerconnects, both