Free-form optimization of nanophotonic devices: from classical methods to deep learning

Area of Science:

• Photonics and Nanotechnology
• Optics and Light Manipulation

Background:

• Nanophotonic devices utilize subwavelength optical elements for high-resolution light control.
• Conventional designs are limited by fixed optical element shapes, hindering full design potential.

Purpose of the Study:

• To systematically review the emerging field of free-form nanophotonic device design.
• To define "free-form" in photonic device design and explore its implications.
• To survey diverse optimization strategies for nanophotonic devices.

Main Methods:

• Overview of classical design methods.
• Discussion of adjoint-based optimization techniques.
• Exploration of contemporary machine-learning-based approaches for nanophotonic design.

Main Results:

• Free-form design schemes offer a departure from conventional constraints in nanophotonics.
• A comprehensive survey of various free-form design strategies is presented.
• The potential for enhanced performance and novel functionalities in nanophotonic devices is highlighted.

Conclusions:

• Free-form design is crucial for realizing the complete potential of nanophotonic devices.
• The field is rapidly growing, with machine learning showing significant promise.
• Further research into free-form optimization will drive innovation in light manipulation technologies.