NANO-IMAGING VIA PERFORMANCE ENHANCED MICROSPHERE NANOSCOPE

Abstract

Optical diffraction effect imposes a radical obstacle for conventional optical microscopes to achieve subwavelength imaging resolution and thus restricts their usages in a multitude of nanoscale applications. Many super-resolution imaging techniques have been developed to overcome the diffraction limit. Among them, microsphere nanoscopes have gained popularity because of their cost-effectiveness and real-time imaging capability. However, the microsphere nanoscope still faces restrictions including limited magnification, relatively low contrast, restricted numerical aperture and short working distance. In this thesis, three technical routes have been proposed to improve these aspects. Microsphere compound lenses are designed to increase the magnification and thus allow the use of low-power objective lenses for nano-imaging. Two types of engineered microspheres are proposed to enhance the imaging contrast and resolution. The physics-assisted deep learning is demonstrated to empower microsphere nanoscopes with elongated working distances. These technical routes promise comprehensive imaging performance enhancement of the microsphere nano-imaging technique.