09/06/2024
By Pamela Morel
Presenter: Hualiang Zhang, Professor, Electrical and Computer Engineering Department, University of Massachusetts Lowell
Date/Time: September 20, 2024 at 1 p.m. EST
This is a virtual meeting. Please email: Pamela_Morel@uml.edu for the Zoom link 72 hours prior to the seminar.
Abstract:
RF and optical devices or systems play important roles in our daily life ranging from wireless communication to imaging. Emerging applications such as 5G/6G, internet-of-things, and autonomous driving have imposed stringent requirements to RF and optical devices. The emerging of new RF and optical components such as metasurfaces has led to new challenges. In this talk, I discuss our research activities in deep learning-based techniques for addressing these technical challenges. First, I will discuss our deep learning modeling approach for predicting the performance of freeform optical metasurface structures. Our neural network approach overcomes two key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch and accurate EM-wave phase prediction. Second, to demonstrate the capability of deep learning techniques for complex and non-intuitive metasurface design, I will present a novel conditional generative network that can achieve meta-atom/metasurface designs based on different performance requirements. Applications of these deep learning networks will also be discussed. Lastly, the application of deep-learning techniques for RF components design will be explored and showcased.
Biosketch:
Hualiang Zhang, Ph.D., is a professor at the Electrical and Computer Engineering Department, University of Massachusetts Lowell. He received his B.S. degree in Electrical Engineering from the University of Science and Technology of China in 2003. He received his Ph.D. degree in Electrical and Computer Engineering from the Hong Kong University of Science and Technology in 2007. From 2007 to 2009 he was a postdoctoral research associate in the Department of Electrical and Computer Engineering at the University of Arizona. His current teaching and research interests are on high frequency circuits, components, and systems, enabled by advanced computational techniques, new materials, and innovative manufacturing technologies. Applications of his research include wireless communications (e.g. Satcom, 5G and 6G), internet-of-things, radar sensing, wearable electronics, energy-efficient electronic systems, and optical imaging and sensing systems, ranging from RF/microwave/millimeter-wave to infrared and even beyond. He has published 300 refereed journal and conference papers, as well as 1 book chapter and 6 patents (4 issued, and 2 pending) in related research topics. He has been actively involved in organizing conferences and workshops on microwave, antennas, and wireless devices and systems. He is an associate editor of Wiley's International Journal of Numerical Modelling - Electronic Networks, Devices and Fields. He received the 2018 ECE Department Teaching Excellence award. Dr. Zhang is a senior member of IEEE.