DISTINGUISHED LECTURERS LIVE WEBINAR SERIES 2025

Emerging AI/Machine Learning Technologies for Analysis and Optimization in High-Speed/High-Frequency Packages and Systems

WEDNESDAY, June 25, 2025 • 4:00 - 5:30PM EDT

AI and machine learning are unconventional technologies with unique capability to address challenges in electromagnetic-based analysis and optimization in high-speed/high-frequency electronic components/packages and systems. With phenomenal progress in electromagnetic based computation algorithms, along with dramatic changes in the computing environment, high-fidelity electromagnetic models are now an important part of high-speed/high-frequency electromagnetic design automation. However new design challenges continue to rise. Electromagnetic structures and circuits are becoming more complex, and frequency is getting higher. More sophistication in multiphysics modeling and design are becoming increasingly necessary. Meaningful design problems easily become computationally prohibitive.

In this talk, we present AI/machine learning technologies for electromagnetic /multiphysics based modeling and optimization, and their applications to signal/power integrity analysis of highspeed/high-frequency electronic packages and subsystems. We will highlight emerging directions of knowledge-based, cognition-driven design. Incorporating domain-specific design knowledge/engineering equations into artificial neural networks, knowledge-based and deep-learning based computational technologies are producing fine-grained modeling and design solutions for problems which are otherwise computationally very expensive. New formulations of inverse neural network training algorithms allow instant solutions to electromagnetic inverse modeling problems addressing the technical challenges of non-uniqueness in inverse modeling. Emerging machine learning structures and optimization algorithms for electromagnetic based design and signal/power integrity analysis will be discussed.

FEATURED DISTINGUISHED LECTURER:

Prof. Qi-jun Zhang, Chancellor’s Professor, Department of Electronics, Carleton University

Dr. Zhang received the B.Eng. degree in EE from Nanjing University of Science and Technology (Nanjing) in 1982, and the Ph,D. degree in EE from McMaster University (Hamilton, Ontario) in 1987. He was a researcher engineer in Optimization Systems Associates Inc. (Dundas, Ontario) during 1988-1990 developing advanced microwave optimization software. He joined the Department of Electronics, Carleton University in 1990, where he is currently a Chancellor’s Professor.

Dr. Zhang’s research area is AI/machine learning, and optimization for designing high-speed/high-frequency components/packages and systems, which are building blocks of computers, wireless and wired systems in telecommunications, internet, and intelligent and autonomous systems.

Dr. Zhang advanced the theory and practice of microwave active and passive computer-aided design with innovations in linear and nonlinear device modeling and circuit optimization for which he received the IEEE Fellowship in 2006.

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TUESDAY, JULY 15, 2025 • 10:00 - 11:30 PM EDT

WEDNESDAY, JULY 30, 2025 • 4:00 - 5:30 PM EDT

WEDNESDAY, AUGUST 27, 2025 • 12:00 - 1:30 PM EDT

WEDNESDAY, SEPTEMBER 17, 2025 • 4:00 - 5:30 PM EDT

WEDNESDAY, OCTOBER 1, 2025 • 4:00 - 5:30 PM EDT

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