Deep Learning Enabled Design of Terahertz High-Q Metamaterials

TL;DR

This paper introduces deep learning models for the efficient design and prediction of high-Q terahertz metamaterials, significantly improving accuracy and speed over traditional methods, enabling advanced applications in communication and sensing.

Contribution

It presents the Electromagnetic Response Transformer and Visual Attention Network models for accurate forward prediction and inverse design of high-Q terahertz metamaterials, outperforming existing techniques.

Findings

ERT achieves highly accurate spectral predictions from structural parameters.

VAN enables precise inverse design of metamaterials with high-Q resonances.

ERT is 4000 times faster than traditional full wave simulations.

Abstract

Metamaterials open up a new way to manipulate electromagnetic waves and realize various functional devices. Metamaterials with high-quality (Q) resonance responses are widely employed in sensing, detection, and other applications. Traditional design of metamaterials involves laborious simulation-optimization and limits the efficiency. The high-Q metamaterials with abrupt spectral change are even harder to reverse design on-demand. In this paper, we propose novel solutions for designing terahertz high-Q metamaterials based on deep learning, including the forward prediction of spectral responses and the inverse design of structural parameters. For the forward prediction, we develop the Electromagnetic Response Transformer (ERT) model to establish the complex mapping relations between the highly sensitive structural parameters and the abrupt spectra, and realize precise prediction of the high-Q resonance in terahertz spectra from given structural parameters. For the inverse design, we introduce the Visual Attention Network (VAN) model with a large model capability to attentively learn the abrupt shifts in spectral resonances, which can efficiently reduce errors and achieve highly accurate inverse design of structural parameters according to the expected high-Q resonance responses. Both models exhibit outstanding performance, and the accuracy is improved one or two orders higher compared to the traditional machine learning methods. Besides, our ERT model can be 4000 times faster than the conventional full wave simulations in computation time. Our work provides new avenues for the deep learning enabled design of terahertz high-Q metamaterials, which holds potential applications in various fields, such as terahertz communication, sensing, imaging, and functional devices.