Inverse Design of Multistructured Terahertz Metamaterial Sensors Based on Improved Conditional Generative Network
Hongyi Ge, Yuwei Bu, Xiaodi Ji, Yuying Jiang, Keke Jia, Yujie Zhang, Yujie Zhang, Yuan Zhang, Yuan Zhang, Xuyang Wu, Qingcheng Sun
Abstract
The terahertz (THz) metamaterial sensor design is typically complex and requires substantial expertise in physics. To simplify this process, we propose a novel reverse design model based on an improved conditional generative adversarial network that integrates self-attention generative adversarial network and Wasserstein generative adversarial network (WGAN) networks, and is referred to as the self-attention conditional Wasserstein GAN (SACW-GAN) model. By using the target response of the sensor as the input to the generator network, and incorporating labeling information, an attention mechanism, and the Wasserstein distance, we achieve effective reverse design of THz metamaterial sensors. The simulation results demonstrate the model's high performance, with spectral and image accuracies of 95% and 97%, respectively. This deep learning approach offers new perspectives and methodologies for the reverse design and application of THz metamaterial sensors, significantly advancing the field.