Machine Learning and Application in Terahertz Technology: A Review on Achievements and Future Challenges
Yuying Jiang, Guangming Li, Hongyi Ge, Faye Wang, Li Li, Xinyu Chen, Ming Lu, Yuan Zhang
Abstract
Terahertz (THz) radiation (0.1~10 THz) shows great potential in agricultural products detection, biomedical, and security inspection in recent years. Machine learning methods are widely used to support the user demand of higher efficiency and high prediction accuracy. The technological and key challenges of machine learning methods are for THz spectroscopy and image data preprocessing, reconstruction algorithms, and qualitative and quantitative analysis. In this paper, an exhaustive review of recent related works of THz detection and imaging techniques and machine learning methods are presented. The application of machine learning methods combined with THz technology in quality inspection of agricultural products, biomedical, security inspection, and materials science are highlighted. Challenges of machine learning methods for these applications are addressed. The development trend and future perspectives of THz technology are also discussed.