Field-material coupled neural network: A novel prior-free and data-free inverse problem solver for extracting complex dielectric constant in terahertz band
Bundesanstalt für Materialforschung und -prüfung (BAM)（联邦材料研究与测试研究所）；Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila（拉奎拉大学工业与信息技术工程及经济系）；Faculty of Environmental and Technology Manufacturing, Technical University in Zvolen（兹沃伦技术大学环境与技术制造学院）；Geology Division, School of Science and Technology (SST), Università degli Studi di Camerino（卡梅里诺大学科学与技术学院地质学部）；Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory (CVSL), Laval University（拉瓦尔大学电气与计算机工程系计算机视觉与系统实验室）
Pengfei Zhu, Stefano Sfarra, Elena Pivarciova, Carlo Santulli, Xavier Maldague
Accurate extraction of the complex dielectric constant in the terahertz (THz) band is essential for material characterization and non-destructive evaluation yet remains challenging due to the ill-posed nature of electromagnetic inverse problems and the limited availability of reliable reference data. In this work, a field-material couple neural network (FMCNN) is proposed to retrieve the complex dielectric constant directly from THz measurements. The FMCNN consists of a field neural network and a material neural network that are strongly coupled through the frequency-domain Maxwell equations in the form of a Helmholtz equation, with the governing physics enforced by partial differential equation (PDE) and boundary condition constraints. This formulation enables prior-free and data-free inversion, requiring only measured test data as input. The extracted dielectric constants are validated by comparison with results from a one-dimensional normal-incidence model and the Drude-Lorentz model, showing good agreement over a broad frequency range, particularly above 0.2 THz. These results demonstrate that the FMCNN provides a physics-consistent and data-efficient approach for material parameter extraction in the THz band, offering an alternative to conventional model-based methods.
Near-Field Coupling of Polypropylene Dielectric Waveguide Routed Near PCB Board at Terahertz Frequencies
School of Integrated Circuits and Electronic, Beijing Institute of Technology（北京理工大学集成电路与电子学院）；State Key Laboratory of Environment Characteristics and Effects for Near-space（临近空间环境特性及效应国家重点实验室）；School of Electronic and Information Engineering, Hebei University of Technology（河北工业大学电子信息工程学院）；Advanced Laser Technology Research Center（先进激光技术研究中心）
