Neural Networks in Photonics: Revolutionizing Anti-Reflective Coating Design

Neural networks (NNs) have emerged as a groundbreaking tool in photonics, significantly advancing the design of anti-reflective (AR) coatings for the visible spectrum (۴۰۰-۷۰۰ nm). By leveraging advanced architectures such as physics-informed neural networks (PINNs) and graph neural networks (GNNs), researchers have achieved unprecedented precision in simulating optical properties, predicting coating performance, and optimizing multilayer structures. These methodologies have enabled the prediction of reflectivity spectra with less than ۱% error and the optimization of layer thicknesses within ±۱.۲ nm, setting new benchmarks in the field. This study focuses on the design and optimization of multilayer anti-reflective (AR) coatings applied to glass substrates, utilizing neural networks (NNs) to predict layer-specific parameters such as refractive indices, thicknesses, and molar compositions. The bottom layer is pure silicon dioxide (SiO۲), while the top layer is pure titanium dioxide (TiO۲) to leverage its photocatalytic properties. Intermediate layers are engineered with tunable thicknesses and refractive indices, adhering to physical principles such as quarter-wavelength thickness and refractive index progression. As an example, a five-layer coating is optimized using NN predictions.

کلیدواژه ها:

Neural networks ، Anti-reflective coatings ، Inverse design ، Photonic optimization ، Multilayer systems