From Equations to Algorithms and Data: Transforming Microwave Engineering and Education with Machine Learning

TL;DR

This paper advocates for a pedagogical shift in microwave engineering education by integrating machine learning and data-driven electromagnetic synthesis to enable topology-agnostic, performance-oriented design exploration.

Contribution

It introduces a machine-learning-based framework for electromagnetic synthesis that enhances physical intuition and aligns education with modern high-frequency system design.

Findings

Enables topology-agnostic, performance-driven microwave circuit design.

Integrates inverse design and multi-objective optimization into curriculum.

Improves student engagement and understanding of electromagnetic behavior.

Abstract

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.