Abstract
The design and fabrication of nanoscale multilayered thin films play an essential role in regulating the operation efficiency of sensitive optical sensors and filters. In this paper, we introduce a packaged tool that employs flexible electromagnetic calculation software with machine learning in order to find the optimized double-band antireflection coatings in intervals of wavelength from 3 to 5 µm and 8 to 12 µm. Instead of computing or modeling an extremely enormous set of thin film structures, this tool enhanced with machine learning can swiftly predict the optical properties of a given structure with $\gt 99.7\%$ accuracy and a substantial reduction in computation costs. Furthermore, the tool includes two learning methods that can infer a global optimal structure or suitable local optimal ones. Specifically, these well-trained models provide the highest accurate double-band average transmission coefficient combined with the lowest number of layers or the thinnest total thickness starting from a reference multilayered structure. Finally, the more sophisticated enhancement method, called the double deep Q-learning network, exhibited the best performance in finding optimal antireflective multilayered structures with the highest double-band average transmission coefficient of about 98.95%.
© 2022 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Yongxiang Zhao, Fei Chen, Qiang Shen, and Lianmeng Zhang
Appl. Opt. 53(23) 5222-5229 (2014)
Alexander Luce, Ali Mahdavi, Florian Marquardt, and Heribert Wankerl
J. Opt. Soc. Am. A 39(6) 1007-1013 (2022)
Hailan Wang, Chenying Yang, Yusi Wang, Wenjia Yuan, Tingting Zheng, Xiao Chen, Yujie Liu, Yueguang Zhang, and Weidong Shen
Opt. Express 30(16) 28922-28931 (2022)