IMS2026 Technical Program

This inter-society technical panel will emphasize the urgent need for sustainable growth within the RF industry, particularly through the development of standards for measuring the carbon footprint of RF technologies. Today, the environmental impact of RF systems extends across the full lifecycle—from manufacturing processes and material usage to deployment, energy consumption, and long-term operation. However, the absence of consistent measurement frameworks makes it difficult to evaluate, compare, and ultimately reduce these impacts in a systematic way. The panel will bring together experts from multiple societies to explore how collective action can establish widely accepted methodologies and best practices for carbon footprint assessment in RF technologies. By working across organizational boundaries, societies can not only help define these standards but also provide strategic guidance to industry, academia, and policymakers. Such efforts are critical to ensuring that sustainability becomes a foundational consideration in future RF innovations rather than an afterthought. Ultimately, the discussion will highlight how professional societies can play a pivotal role in shaping a greener future for the RF industry—by fostering collaboration, driving standardization, and offering direction to reduce carbon emissions across both manufacturing and operational domains.

Artificial Intelligence is revolutionizing microwave circuit design, just as it is transforming other scientific and industrial domains. The growing number of published research papers demonstrates that the microwave community is actively embracing AI and ML across a wide spectrum of applications—from novel device modeling to virtual data generation, data management, and advanced EDA tools for circuit optimization. New commercial solutions for ML-assisted circuit design, already offer first-pass, fully automated layout generation, multi-objective optimization, and seamless multi-platform integration from device to system level. This evolving landscape suggests a progressive shift in researchers' focus from traditional design practices toward a complex interplay involving the development of custom, high-accuracy, dynamically reconfigurable models, advanced EDA algorithms, and ML workflows. Are we ready for this revolution? Can we truly trust AI/ML-driven design? Will AI really help to uncover entirely new device concepts and circuit topologies, or will it remain a highly capable design assistant? What tools and skills are needed to become active contributors in this new paradigm? This panel will bring together experts from foundries, model development, and EDA vendors to critically examine the pros and cons, practical implications, IP constraints and future directions of AI-assisted microwave circuit design.