Intelligent metaphotonics empowered by machine learning

TL;DR

This paper explores how machine learning enhances metaphotonics, enabling smarter design and understanding of light-matter interactions in advanced photonic systems like metasurfaces and nanoantennas.

Contribution

It introduces the emerging field of intelligent metaphotonics, demonstrating how AI techniques can be applied to fundamental physics and device design in metaphotonics.

Findings

Machine learning accelerates the design of metasurfaces.

AI provides new insights into light-matter interactions.

Enhanced control over optical resonances in metaphotonics.

Abstract

In the recent years, we observe a dramatic boost of research in photonics empowered by the concepts of machine learning and artificial intelligence. The corresponding photonic systems, to which this new methodology is applied, can range from traditional optical waveguides to nanoantennas and metasurfaces, and these novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Concepts and approaches of artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by optically-induced electric and magnetic resonances. Here, we introduce this new field with its application to metaphotonics and also present a summary of the basic concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.