A new multilayer optical film optimal method based on deep q-learning

TL;DR

This paper introduces a deep Q-learning based algorithm for designing multi-layer optical films, achieving global optimal solutions and improving design efficiency over traditional methods.

Contribution

The paper presents a novel deep Q-learning approach for optical film design that overcomes local optima issues of traditional algorithms.

Findings

Achieves global optimal solutions for optical film structures.

Significantly improves design efficiency.

Outperforms traditional algorithms in convergence quality.

Abstract

Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and manufacture of multi-layer films. However, traditional numerical algorithms of converge to local optimum. This means that the algorithms can not give a global optimal solution to the material researchers. In recent years, due to the rapid development to far artificial intelligence, to optimize optical film structure using AI algorithm has become possible. In this paper, we will introduce a new optical film design algorithm based on the deep Q learning. This model can converge the global optimum of the optical thin film structure, this will greatly improve the design efficiency of multi-layer films.