Artificial Neural Networks for RF Passive-Device Modeling and Design: A Review

Abstract

Artificial neural networks (ANNs) offer efficient surrogates for radio-frequency (RF) passive-device modeling and design, but prediction accuracy alone does not establish their engineering reliability. This review examines four overlapping analytical perspectives: data generation and sampling, forward modeling, knowledge-enhanced modeling, and inverse modeling and design. Its passive-device-focused synthesis separates computational costs by workflow stage, distinguishes physical-property guarantees from enforcement and tests, and examines the feasibility and coverage of inverse-design solutions. Direct passive-device applications are distinguished from transferable electromagnetic methods. Across the reviewed workflows, adaptive sampling reduced the need for costly labeling, while structured and hybrid representations improved response approximation within the tested domains. Multivalued and generative inverse models accommodated non-unique solutions, although candidate diversity should be assessed separately from feasibility and solution coverage. Coarse models, transfer functions, and physics-informed losses provide useful structural priors but do not automatically guarantee network properties or reliable extrapolation. The experimental results demonstrated the practical potential for the tested devices and processes. These findings call for reproducible benchmarks and process-aware validation that link modeling accuracy to physical reliability and engineering applicability.