In:
Applied Optics, Optica Publishing Group, Vol. 61, No. 12 ( 2022-04-20), p. 3328-
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 〉 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%.
Type of Medium:
Online Resource
ISSN:
1559-128X
,
2155-3165
Language:
English
Publisher:
Optica Publishing Group
Publication Date:
2022
detail.hit.zdb_id:
207387-0