Optical Multilayer Thin Film Structure Inverse Design: From Optimization to Deep Learning

TL;DR

This paper reviews the evolution of inverse design methods for optical multilayer thin film structures, highlighting traditional optimization and recent deep learning techniques, discussing challenges and future directions.

Contribution

It provides a comprehensive overview of recent progress and compares different algorithms used for inverse design in multilayer thin films.

Findings

Deep learning approaches outperform traditional optimization in speed.

Various algorithms address specific challenges in inverse design.

The paper identifies future research directions in the field.

Abstract

Optical multilayer thin film structures have been widely used in numerous photonic domains and applications. The key component to enable these applications is the inverse design. Different from other photonic structures such as metasurface or waveguide, multilayer thin film is a one-dimensional structure, which deserves its own treatment for the design process. Optimization has always been the standard design algorithm for decades. Recent years have witnessed a rapid development of integrating different deep learning algorithms to tackle the inverse design problems. A natural question to ask is: how do these algorithms differ from each other? Why do we need to develop so many algorithms and what type of challenges do they solve? What is the state-of-the-art algorithm in this domain? Here, we review recent progress and provide a guide-tour through this research area, starting from traditional optimization to recent deep learning approaches. Challenges and future perspectives are also discussed.