Artificial Intelligence-Enhanced Metasurfaces for Instantaneous Measurements of Dispersive Refractive Index

Measurements of the refractive index of liquids are in high demand in numerous fields such as agriculture, food and beverages, and medicine. However, conventional ellipsometric refractive index measurements are too expensive and labor-intensive for consumer devices, while Abbe refractometry is limited to the measurement at a single wavelength. Here, a new approach is proposed using machine learning to unlock the potential of colorimetric metasurfaces for the real-time measurement of the dispersive refractive index of liquids over the entire visible spectrum. The platform with a proof-of-concept experiment for measuring the concentration of glucose is further demonstrated, which holds a profound impact in non-invasive medical sensing. High-index-dielectric metasurfaces are designed and fabricated, while their experimentally measured reflectance and reflected colors, through microscopy and a standard smartphone, are used to train deep-learning models to provide measurements of the dispersive background refractive index with a resolution of approximate to 10-4, which is comparable to the known index as measured with ellipsometry. These results show the potential of enabling the unique optical properties of metasurfaces with machine learning to create a platform for the quick, simple, and high-resolution measurement of the dispersive refractive index of liquids, without the need for highly specialized experts and optical procedures. Artificial intelligence is used to process spectral information from structural color metasurfaces to measure the dispersive refractive index of liquids. Images from a smartphone camera are demonstrated to measure the concentration of glucose in real time using a well-trained neural network. The data-driven system shows resolution comparable to conventional measurement techniques. image