Applying GSTCs in Electromagnetic Surface Inverse Design Using Optimization and Machine Learning

Abstract

Electromagnetic metasurface design based on far-field constraints without the knowledge of the fields on both sides of the metasurface is typically a time consuming and iterative process, which relies heavily on heuristics and ad hoc methods. This paper proposes an end-to-end systematic and efficient approach where the designer inputs the desired far-field parameters such as nulls, sidelobe levels, and main beam level(s); and a 3-layer non-uniform passive, lossless, omega-type bianisotropic electromagnetic metasurface design to satisfy them is returned. The surface parameters to realize the far-field criteria are found using the alternating direction method of multipliers on a homogenized model derived from the method of moments. This model incorporates edge effects and mutual coupling. Optimization through the physical unit cell space integrated with machine learning-based surrogate models is used to realize the desired surface parameters with physical meta-atom (or unit cell) designs. A passive and lossless multi-beam example is shown to demonstrate the effectiveness of this method.