Deep learning and inverse design of artificial electromagnetic materials

Abstract

Deep neural networks are empirically derived systems that have transformed research methods and are driving scientific discovery. Artificial electromagnetic materials, such as electromagnetic metamaterials, photonic crystals, and plasmonics, are research fields where deep neural network results evince the data driven approach; especially in cases where conventional computational and optimization methods have failed. We propose and demonstrate a deep learning method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated.