Deep learning in nano-photonics: inverse design and beyond

TL;DR

This review critically examines deep learning methods in nano-photonics, focusing on inverse design and other applications, highlighting progress, challenges, and future directions in the field.

Contribution

It provides a comprehensive classification and critical analysis of deep learning approaches in nano-photonics, covering both inverse design and broader applications.

Findings

Deep learning enables efficient inverse design of photonic structures.

Various applications of machine learning extend beyond inverse design, including simulation acceleration and imaging.

The review identifies strengths and weaknesses of current approaches.

Abstract

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community's attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics "beyond inverse design". This spans from physics informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and "knowledge discovery" to experimental applications.