Intelligent Nanophotonics: Merging Photonics and Artificial Intelligence at the Nanoscale

TL;DR

This paper reviews how integrating machine learning, especially deep learning, with nanophotonics enhances the design and optimization of nanoscale photonic devices, offering more efficient solutions and new research directions.

Contribution

It provides a comprehensive overview of recent advances in applying machine learning to nanophotonics, focusing on inverse design and implementation on photonic platforms.

Findings

Deep learning accelerates nanophotonic device design.

Machine learning enables efficient exploration of complex parameter spaces.

Future perspectives on nanophotonics and AI integration are discussed.

Abstract

Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.