A newcomer's guide to deep learning for inverse design in nano-photonics

Area of Science:

• Nanophotonics and Optics
• Computational Science
• Machine Learning

Background:

• Designing nanophotonic devices for precise light control is complex and computationally intensive.
• Traditional iterative methods for nanophotonic inverse design are time-consuming.
• Deep learning (DL) offers a promising alternative for accelerating nanophotonic device design.

Purpose of the Study:

• To provide a comprehensive tutorial on applying deep learning to nanophotonic inverse design for researchers new to DL.
• To offer practical guidance, workflows, and design considerations for implementing DL in nanophotonics.
• To bridge the gap between DL advancements and their application in scientific research.

Main Methods:

• Introduction to fundamental deep learning concepts relevant to inverse design.
• Critical discussion of deep learning benefits for nanophotonic inverse design problems.
• Exploration of iterative and direct deep learning techniques with advantages and limitations.
• Inclusion of Python notebook examples for practical implementation.

Main Results:

• A structured workflow and practical guidelines for applying deep learning in nanophotonics.
• Evaluation of various deep learning-based inverse design techniques.
• Illustrative Python notebooks demonstrating the application of discussed methods.

Conclusions:

• Deep learning provides powerful tools for efficient nanophotonic inverse design.
• This tutorial empowers researchers to leverage deep learning, overcoming initial hurdles.
• The presented methods and examples are applicable to both nanophotonics and other scientific domains.