Shape-Dependent, Deep-Learning-Assisted Metamaterial Solid Immersion Lens (mSIL) Super-Resolution Imaging

Abstract

We present the first systematic comparison of three TiO2 metamaterial solid immersion lens geometries - sub-hemispherical, super-hemispherical, and full-spherical - for label-free super-resolution imaging. Using SEM, we characterised both the cap profiles and the nanoparticle-fluid immersion at the lens-sample interface, revealing that super-hemispherical lenses achieve the deepest immersion and closest contact with sample features. Imaging experiments under wide-field and laser confocal microscopes show that this enhanced immersion drives superior resolution and contrast. In addition, we introduce a deep learning approach based on a SinCUT image translation model to establish a cross-modal mapping between SEM morphology and optical imaging response, enabling virtual optical predictions and providing a first step toward a digital twin representation of mSIL imaging behaviour. Electromagnetic simulations further confirm a direct correlation between immersion depth and far-field main lobe intensity. Our findings demonstrate that careful control of lens shape and nanoparticle-fluid penetration, together with data-driven modelling, is essential to maximise super-resolution performance in TiO2 mSILs.