Mathematical analysis

Materials science

Metamaterial

Physics

Nanostructure

Characterization (materials science)

Inverse problem

Nanotechnology

Pure mathematics

Mathematics

Optics

Electromagnetic field

Quantum mechanics

Computer science

Plasmon

Field (mathematics)

Nanophotonics

Photonics

Light Science & Applications

Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.

Plasmonic nanostructure design and characterization via Deep Learning