Inverse-Design of RF and Optical Meta-Devices using Optimization and Artificial Intelligence

Abstract

Metamaterial and metasurface devices (i.e., meta-devices) have shown tremendous potential for disrupting conventional RF and optical system design due to their ability to tailor the propagation of electromagnetic radiation in a desired fashion. Meta-devices are generally synthesized from “meta-atom” building blocks which often require thousands of simulations in order to optimize their performance. This can make the design process computationally challenging, especially when a large number of design parameters are used. To this end, inverse-design strategies based on multi-objective optimization and deep learning have demonstrated tremendous design acceleration. This talk will introduce fundamental concepts of optimization and deep learning in electromagnetic and optical design as well as showcase a number of designs ranging from RF meta-radomes to nanofabricated metalenses.

Bio

Sawyer D. Campbell received the B.S. degree in physics from Illinois Wesleyan University, Bloomington, IL, USA, in 2008, and the M.S. and Ph.D. degrees in optical sciences from the University of Arizona, Tucson, AZ, USA, in 2010 and 2013, respectively. In 2014, he joined Penn State University, first as a Postdoctoral Scholar and then as a Research faculty. He is now Associate Professor and Director of the Physical Optics and Electromagnetics Lab (POEM). He has published over 250 technical papers and proceedings articles, 2 books, and is the author/coauthor of 5 book chapters. He is an Associate Editor for IEEE Access and IEEE Transactions on Antennas and Propagation. His current research interests include metasurfaces, nanophotonics, gradient-index lenses, high power microwave antennas, optimization, and applications of deep learning to RF and optical inverse-design problems.