A Reinforcement learning method for Optical Thin-Film Design

2021/02/13 by An-Qing Jiang, Jiang, Anqing, Liangyao Chen +3 · 1 citation
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Optics (physics.optics) #Thin-Film Transistor Technologies #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2102.09398

openalex publication_date 2021/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning(RL) and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.

Citations

• Automated Optical Multi-layer Design via Deep Reinforcement Learning

Cited by

• Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

Related

• Optical Multilayer Thin Film Structure Inverse Design: From Optimization to Deep Learning
• Automated Optical Multi-layer Design via Deep Reinforcement Learning
• A new multilayer optical film optimal method based on deep q-learning
• General Inverse Design of Thin-Film Metamaterials With Convolutional Neural Networks
• Data driven design of optical resonators