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

TL;DR

This paper systematically compares three TiO2 metamaterial solid immersion lens geometries for label-free super-resolution imaging, demonstrating that super-hemispherical lenses provide superior resolution and contrast, and introduces a deep learning model for virtual optical predictions.

Contribution

It is the first to compare different TiO2 mSIL geometries systematically and to integrate a deep learning approach for cross-modal imaging prediction.

Findings

Super-hemispherical lenses achieve the deepest immersion and best contact.

Enhanced immersion correlates with improved resolution and contrast.

Deep learning enables virtual optical predictions from SEM morphology.

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.