Over 30,000-fold field enhancement of terahertz nanoresonators enabled by rapid inverse design

TL;DR

This paper presents a physics-informed machine learning method for rapidly designing terahertz nanoresonators, achieving over 30,000-fold field enhancement with significantly reduced computational resources.

Contribution

It introduces a novel inverse design approach using double deep Q-learning and an analytical model to efficiently optimize THz nanogap loop arrays.

Findings

Achieved 32,000-fold electric field enhancement at 0.2 THz.

Reduced design time to about 39 hours on a personal computer.

Demonstrated a 300% improvement over previous results.

Abstract

The rapid development of 6G communications using terahertz (THz) electromagnetic waves has created a demand for highly sensitive THz nanoresonators capable of detecting these waves. Among the potential candidates, THz nanogap loop arrays show promising characteristics but require significant computational resources for accurate simulation. This requirement arises because their unit cells are 10 times smaller than millimeter wavelengths, with nanogap regions that are 1,000,000 times smaller. To address this challenge, we propose a rapid inverse design method using physics-informed machine learning, employing double deep Q-learning with an analytical model of the THz nanogap loop array. In about 39 hours on a middle-level personal computer, our approach identifies the optimal structure through 200,000 iterations, achieving experimental electric field enhancement of 32,000 at 0.2 THz, 300 % stronger than prior results. Our analytical model-based approach significantly reduces computational resources, offering a practical alternative to numerical simulation-based inverse design