Beyond application-specific design: a generalized deep learning framework for optical property prediction in TiO2/GaN nanophotonic metasurfaces

Abstract

Metalenses have garnered significant attention for their remarkable ability to precisely focus light while obviating the inconvenience and intricacy associated with conventional curved lenses. Identifying the best response for these phase gradient optical devices necessitates intensive trial and error analysis of meta-atoms with various shapes, materials and dimensions. In this work, we present an artificial intelligence-based framework to predict the highly skewed, complex transmission and phase responses of the constituent nanorods. Here, we employed a transfer learning model to train on two extensive datasets comprising the optical responses of gallium nitride and titanium dioxide nanopillars, each integrated onto silica substrates. The accuracy of the dataset was assessed through experimental investigation, particularly inspecting transmittance and the refractive index for a TiO₂ layer of a certain height. A reasonable agreement has been obtained for both cases. The optimized algorithm estimates the performance in terms of amplitude and phase, attaining minimum Mean Squared Error (MSE) values of 2.3 × 10⁻⁶ and 1.31 × 10⁻⁵, respectively, for a wavelength range of 600–700 nm. To validate the effectiveness of our proposed approach, focusing performance was exhibited for two flat lenses: a smaller lens with a 20 μm diameter and a larger lens featuring an identical diameter and focal length of 100 μm. A brief study on the effects of varying angles of incident light has also been conducted. While minimizing the need for typically tedious and at times ineffective repetitive analyses, the parameterized datasets can be beneficial for developing different optical components.