Metasurface inverse design using machine learning approaches

Conventional metasurface design methods usually require a lot of computational resources and time, meaning they fail to satisfy the efficient, rapid design on demand. On account of this, we branch out of the conventional metasurface design methods by attempting to relate the emerging discipline of artificial intelligence to a traditional physical area. With our method, named AMID, metasurface structures are designed inversely where they can be computed directly by simply proposing and inputting the desired design targets into AMID. AMID greatly simplifies conventional methods that call for not only sufficient professional knowledge but also trial and error through simulation softwares. According to the design results, unit cells of metasurfaces are successfully computed, which verifies the availability of AMID and improves the design efficiency in the meanwhile. (paper)