Machine-learning reinforcement for optimizing multilayered thin films: applications in designing broadband antireflection coatings.

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.

A New Simple Analytical Method for a Highly Accurate Determination of the Optical Parameters of a Slab from Transmittance Data.

To date, determining with high accuracy the optical parameters (extinction coefficient and refractive index ) of a slab from the sole transmittance data requires an inverse method based on numerical iteration procedures. In this paper, we propose a new inverse analytical method of extracting (, ) without numerical iterative processes. The high accuracy of this new inverse method is assessed, and as an application example, the optical parameters of CaF and Si substrates are determined in the IR spectral range of 4-8 µm.