Lindemans, YensYensLindemansUllrick, ThijsThijsUllrickCouckuyt, IvoIvoCouckuytPattyn, TimTimPattynDeschrijver, DirkDirkDeschrijverVande Ginste, DriesDriesVande GinsteDhaene, TomTomDhaene2026-03-122026-03-122025-09-012156-3950https://imec-publications.be/handle/20.500.12860/58823Microwave device design increasingly relies on surrogate modeling to accelerate optimization and reduce costly electromagnetic (EM) simulations. This article presents a spectral Bayesian optimization (SBO) framework leveraging a physics-informed 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 (BO) can significantly improve microwave device design efficiency.engJournal article10.1109/TCPMT.2025.3592441WOS:001575776900003