From Equations to Algorithms and Data: Transforming Microwave Engineering and Education with Machine Learning
For microwave engineering educators and students, this pedagogical shift aims to modernize curriculum and align with industrial practices, but the proposal is conceptual without empirical validation.
The paper proposes integrating machine-learning-based inverse design and data-driven electromagnetic synthesis into microwave engineering education to address challenges in millimeter-wave and terahertz regimes, enabling topology-agnostic, performance-oriented design exploration.
Conventional microwave engineering education relies heavily on analytical methods, canonical circuit topologies, and intuition-driven design, which have proven effective at microwave frequencies. However, as systems increasingly operate in the millimeter-wave and terahertz regimes, parasitic effects, process-dependent electromagnetic interactions, and ultra-wideband performance requirements challenge both topology/layout-constrained traditional design methodologies and existing teaching paradigms. This paper proposes a pedagogical shift in microwave and RFIC (Radio Frequency Integrated Circuit) engineering and education by introducing machine-learning (ML) and data-driven electromagnetic synthesis as a complementary design framework for microwave circuits such as power dividers and combiners, couplers, and baluns. Rather than emphasizing predefined topologies, the proposed approach enables topology-agnostic, performance-oriented exploration of the design space, allowing students to directly engage with electromagnetic behavior through specification-driven synthesis. By integrating machine-learning-based inverse design and multi-objective optimization into the curriculum, the framework enhances physical intuition, encourages design creativity, and better aligns microwave education with emerging industrial practices in high-frequency and ultra-wideband system design.