Deep learning enabled wavefront shaping in complex cavities with a binary tunable metasurface

Modern electronics have become more densely populated due to miniaturization and are expected to perform in increasingly complex environments. These environments give rise to extreme electromagnetic interference through noise and unwanted coupling between components. The ability to isolate or reject interference and to do so intelligently is critical for practical applications. We previously demonstrated the ability to create nulls in the transmission coefficient or induce coherent perfect absorption states at arbitrary frequencies with a binary programmable metasurface[Frazier, Antonsen, Anlage, and Ott, "Wavefront Shaping with a Tunable Metasurface: Creating Coldspots and Coherent Perfect Absorption at Arbitrary Frequencies”, arXiv:2009.05538, https://arxiv.org/abs/2009.05538]. In this work, we show how deep learning can be leveraged to optimize the metasurface commands without relying on a blind iterative optimization approach.