Machine-learning-enabled multi-frequency synthesis of space-time-coding digital metasurfaces

Abstract

Digital metasurfaces based on space‐time coding have established themselves as a powerful and versatile platform for joint spatial/spectral control of electromagnetic waves. However, their advanced design remains a largely open problem with significant computational challenges. This study introduces a novel approach, based on deep neural networks, to address this challenge. The proposed technique enables the simultaneous and independent multi‐frequency synthesis of scattering patterns, allowing precise tailoring of the harmonic equivalent currents (both in magnitude and phase), and enhancing spectral efficiency. These results, experimentally validated at X‐band microwave frequencies, substantially broaden the capabilities of space‐time coding digital metasurfaces, paving the way for advanced applications in wireless communications, sensing, and imaging.