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 for terahertz
nanoresonators using physics-informed machine learning, specifically employing
double deep Q-learning combined with an analytical model of the THz nanogap
loop array. Through approximately 200,000 iterations in about 39 hours on a
middle-level personal computer (CPU: 3.40 GHz, 6 cores, 12 threads, RAM: 16 GB,
GPU: NVIDIA GeForce GTX 1050), our approach successfully identifies the optimal
structure, resulting in an experimental electric field enhancement of 32,000 at
0.2 THz, 300% stronger than previous achievements. By leveraging our analytical
model-based approach, we significantly reduce the computational resources
required, providing a viable alternative to the impractical numerical
simulation-based inverse design that was previously impractical