English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Electromagnetic metasurfaces have attracted significant interest recently dueto their low profile and advantageous applications. Practically, manymetasurface designs start with a set of constraints for the radiated far-field,such as main-beam direction(s) and side lobe levels, and end with a non-uniformphysical structure for the surface. This problem is quite challenging, sincethe required tangential field transformations are not completely known whenonly constraints are placed on the scattered fields. Hence, the requiredsurface properties cannot be solved for analytically. Moreover, the translationof the desired surface properties to the physical unit cells can betime-consuming and difficult, as it is often a one-to-many mapping in a largesolution space. Here, we divide the inverse design process into two steps: amacroscopic and microscopic design step. In the former, we use an iterativeoptimization process to find the surface properties that radiate a far-fieldpattern that complies with specified constraints. This iterative processexploits non-radiating currents to ensure a passive and lossless design. In themicroscopic step, these optimized surface properties are realized with physicalunit cells using machine learning surrogate models. The effectiveness of thisend-to-end synthesis process is demonstrated through measurement results of abeam-splitting prototype.

Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning