Deep Learning Design for Multiwavelength Infrared Image Sensors Based on Dielectric Freeform Metasurface

Abstract

Near-infrared multispectral imaging technology enhances target detection and recognition by distinguishing the spectral characteristics of various targets. However, traditional imaging systems heavily rely on complex optical filter designs that are often bulky and mechanically unstable, posing significant challenges for miniaturization and integration challenging. In this study, a freeform dielectric metasurface with the wavelength-multiplexing focusing effect based on a deep learning model is designed, which can separate the mixed near-infrared light into distinct wavelengths. To effectively modulate the complex amplitude of the transmitted light at three distinct near-infrared wavelengths (1150, 1350, and 1550 nm), high-index silicon freeform nanostructures supporting rich resonant modes are proposed. An inverse design model based on deep learning is utilized to generate individual freeform nanostructures pixel by pixel, satisfying the complex amplitude requirement for a multiplexed metalens design. Both the simulated and experimental results show that the wavelength-multiplexing effect of the devices is in good agreement with the target with negligible crosstalk. Finally, a metasurface is employed to realize near-infrared multispectral imaging, which allows for the distinct detection and decoding of images at the three target wavelengths. The proposed technology has a wide range of applications in clinical medicine, biological tissue imaging, and deep-space exploration.