Neural reparameterization for nonlocal metasurface topology optimization

Abstract

Metasurfaces are emerging as a paradigm for performing all-optical, energy efficient analog computing and image processing in an ultra-compact form-factor. While various operations have been demonstrated with simple metasurface geometries, broadening the range of available processing outputs will require non-trivial and non-intuitive designs. Topology optimization (TO) is a powerful inverse design technique capable of designing such metasurfaces with significantly enhanced optical properties. The two most important design considerations are the quality of the optical performance and the minimum feature size in the resulting device. Larger features can reduce the mismatch between design and r..