Abstract: Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) responses, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this talk, I discuss our research activities in deep learning-based metasurface design, showing how deep learning approaches can be applied for objective-driven meta-atom and meta-device design, opening venues to engineer and realize multifunctional meta-devices and meta-optics. First, I will discuss our deep learning modeling approach for predicting the performance of freeform 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.
Biography: Dr. Hualiang Zhang is an Associate 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. From 2009 to Jan. 2016, he was a faculty member at the University of North Texas. His research interests are on applied electromagnetics and high frequency electronics, enabled by advanced computational techniques, new materials, and innovative manufacturing technologies. He is the co-author of over 220 journal and conference papers. He is an associate editor of Wiley’s International Journal of Numerical Modelling – Electronic Networks, Devices and Fields. Dr. Zhang is a senior member of IEEE.