Deep-learning-assisted designing chiral terahertz metamaterials with asymmetric transmission properties

Chiral metamaterial induced asymmetric transmission (AT) possesses great potential for terahertz (THz) polarization applications, but its design has mainly relied on the conventional trial-and-error forward strategy. Here, based upon massive training samples, we propose a deep-learning-based approach to accelerate the design of chiral metamaterials. The deep learning framework includes two bidirectional networks that allow the model to self-autonomously decipher the nonintuitive relationship between chiral metamaterial structures and their corresponding electromagnetic responses. Our preliminary results show that our model can accurately predict THz responses for any kind of metamaterial structure and inversely retrieve structure parameters from given THz responses. It turns out that the deep-learning-assisted methodology exhibits more efficient design performance than the conventional physical-based metamaterial design approach. This work might provide another artificially intelligent design strategy for chiral metamaterials and shed light on the prosperous development of unprecedented THz applications. (C) 2022 Optica Publishing Group

Automated summary

This study presents a method to accelerate the design of chiral metamaterials using deep learning, which autonomously deciphers the relationship between structure and electromagnetic response based on massive training samples, demonstrating more efficient design performance compared to traditional methods.