KEYWORDS: Deep learning, Thin films, Light absorption, Design and modelling, Multilayers, Film thickness, Thermography, Solar energy, Optical properties, Optical communications
The design of multilayer ENZ stacks is challenging due to the many parameters involved, including the number of layers, thicknesses, ENZ wavelength, and optical losses. Our machine learning-based approach enables us to efficiently search through the vast design space and experimentally verify the performance of the resulting thin film stack. The resulting 2-layered AZO ENZ thin film stack achieved perfect absorption of light (> 95%) in the near-infrared region from 1500 nm to 2500 nm, highlighting the potential of machine learning techniques in designing ENZ materials for a range of applications.
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