Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning

TL;DR

This paper presents MuHAN, a multi-head attention deep learning model that rapidly predicts and designs ultrahigh-Q chiral resonators in metasurfaces, significantly reducing computational time and enabling advanced photonic applications.

Contribution

Introduction of MuHAN, a multi-head attention network that accelerates the design and prediction of ultrahigh-Q chiral resonators with high accuracy and efficiency.

Findings

MuHAN predicts spectral characteristics in ~10ms, vastly faster than traditional simulations.

Achieves 99.85% and 99.9% accuracy for forward and inverse predictions.

Enables inverse design of structures with Q-factors up to 2.9910E5.

Abstract

High quality (Q) factor optical chiral resonators are indispensable for many chiral photonic devices. Designing ultrahigh Q-factors in chiral metasurfaces traditionally relies on extensive parameter scanning, which is time-consuming and inefficient. While deep learning now provides a rapid design alternative, conventional models still face challenges in accurately predicting ultrahigh Q-factor spectral characteristics. In this study, we introduce a multi-head attention network (MuHAN) to accelerate the design of ultrahigh Q-factor optical chiral resonators in bilayer metasurfaces. MuHAN achieves forward spectral predictions in approximately 10ms, thousands of times faster than finite-difference time-domain simulations, boasting 99.85% and 99.9% accuracy for forward and inverse predictions, respectively. By transferring the learned physical principles, we perform inverse design of nanoscale structures with ultrahigh Q-factors (up to 2.9910E5) based on chiral quasi-bound states in the continuum (quasi-BICs) at minimal computational cost. Our rapid design tool, based on MuHAN, enables high-performance encryption imaging, bridging deep learning with high-Q chiral metasurfaces for advanced sensing, laser, and detection applications.