Applications of Metasurfaces in Endoscope and Hyperspectral Imaging

Abstract

Metasurfaces are ultrathin arrays of subwavelength scatterers. They offer versatile control over light within a wavelength-scale thickness. Their ability to condense complex optical functions into compact forms makes them particularly promising for miniaturized imaging systems, where conventional optics face challenges of bulk and limited scalability. This thesis presents several research projects that explore the integration of metasurfaces into endoscopic imaging and hyperspectral imaging, addressing key challenges in device miniaturization, resolution, and spectral functionality. The first part of the thesis focuses on scanning fiber endoscopes (SFEs), which are among the most compact scanning-based endoscopes. Two projects demonstrate the replacement of conventional refractive lens assemblies with metasurface-based flat lenses (metalenses). A monochromatic near-infrared metalens was designed, fabricated, and experimentally validated, achieving diffraction-limited performance and significantly reducing optical track length compared to refractive optics. Building on this, a polychromatic metalens was developed to enable tri-color RGB imaging, overcoming the intrinsic dispersion of conventional metalenses and delivering near-diffraction-limited resolution across multiple wavelengths. These studies highlight the potential of metalenses to enable highly compact endoscopic systems with improved imaging performance. The second part of the thesis extends metasurface functionality beyond focusing to spectral encoding. A metasurface–Fabry–Pérot cavity array was designed as a spatial-to-spectral encoder, enabling the transmission of multi-pixel image information through a single fiber core without scanning. This proof-of-concept demonstrates the feasibility of spectrally encoded, non-scanning endoscopic imaging, offering a pathway to surpass the resolution limits imposed by fiber pixel density in endoscopic imaging. The final part explores hyperspectral imaging, where metasurfaces are used as spectral code masks for compressive sensing–based reconstruction. A metasurface code mask was optimized to encode full hyperspectral datasets into single-shot grayscale images, which were computationally reconstructed to recover high-resolution hyperspectral information. Experimental demonstrations validate this system as a compact, efficient, and high-speed alternative to conventional hyperspectral imagers. Together, these works establish metasurfaces as powerful optical platforms for advancing miniaturized endoscopy and hyperspectral imaging, demonstrating the transformative potential of metasurfaces in next-generation biomedical and imaging technologies.