Deep-Learning Empowered Customized Chiral Metasurface for Calibration-Free Biosensing

Abstract

As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current “one-by-one design” method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.