Multilayer optical thin film design with deep Q learning

Anqing Jiang,Yoshie Osamu,Liangyao Chen

doi:10.1038/s41598-020-69754-w

Anqing Jiang, Yoshie Osamu + Show 1 more

Open Access

https://doi.org/10.1038/s41598-020-69754-w

Copy DOI

Journal: Scientific Reports	Publication Date: Jul 29, 2020
Citations: 35	License type: open-access

Affiliation: Waseda University, Fudan University

Abstract

Multilayer optical film plays a significant role in broad fields of optical application. Due to the nonlinear relationship between the dispersion characteristics of optical materials and the actual performance parameters of optical thin films, it is challenging to optimize optical thin film structure with the traditional models. In this paper, we present an implementation of Deep Q-learning, which suited for the most part for optical thin film. As a set of concrete demonstrations, we optimize solar absorber. The optimal program could optimal this solar absorber in 500 epoch (about 200 steps per-epoch) without any human intervention. Search results perform better than researchers’ manual searches.

Highlights

Multilayer optical film plays a significant role in broad fields of optical application
The optical film researchers developed a variety of global optimization methods used in the design of the film structure
Chang and Lee applied the generalized simulated-annealing method (GSAM) for the thin-film system design and discovered that there would be no problem with the trapping local minimum that happened in the design[3]

Summary

OPEN Multilayer optical thin film design with deep Q learning

Multilayer optical film plays a significant role in broad fields of optical application. The optical film researchers developed a variety of global optimization methods used in the design of the film structure. The researchers worked in this field have applied to the optical coating optimization methods with several models such as particle swarm optimization (PSO)[4], genetic algorithm (GA)[5, 6], ant colony algorithm[7] and deep learning a lgorithm[8,9,10,11,12]. The above research has solved the related physical problems, it needs a lot of known data This supervised learning model can not solve the problem well for some unknown combination problems of materials and structures. This paper will introduce in detail how to combine DQN with optical multilayer film optimization and show the performance of the algorithm in several typical film systems

Optimization target

Conclusions

Author contributions

Findings

Additional information