Super-Resolution Imaging of Sub-diffraction-Limited Pattern with Superlens Based on Deep Learning

Abstract

The development of super-resolution imaging techniques has revolutionized our ability to study the nano-scale world, where objects are often smaller than the diffraction limit of traditional optical microscopes. Super-resolution superlenses have been proposed to solve this problem by manipulating the light wave in the near field. A superlens is a kind of metamaterial-based lens that can enhance the evanescent waves generated by nano-scale objects, utilizing the surface plasmon phenomenon. The superlens allows for the imaging of nano-scale objects that would otherwise be impossible to resolve using traditional lenses. Previous research has shown that nanostructures can be imaged using superlenses, but the exact shape of the superlens must be known in advance, and an analytical calculation is needed to reconstruct the image. Localized plasmon structured illumination microscopy is an approach to achieve super-resolution by imaging the superlens-enhanced evanescent wave with illumination shifts. This study proposes a new approach utilizing a conditional generative adversarial network to obtain super-resolution images of arbitrary nano-scale patterns. To test the efficacy of this approach, finite-difference time-domain simulation was utilized to obtain superlens imaging results. The data from the simulation were then used for deep learning to develop the model. With the help of deep learning, the inverse calculation of complex sub-diffraction-limited patterns can be achieved. The super-resolution feature of the superlens based on deep learning is investigated. The findings of this study have significant implications for the field of nano-scale imaging, where the ability to resolve arbitrary nano-scale patterns will be crucial for advances in nanotechnology and materials science.