Transfer Learning Empowered Multiple-Indicator Optimization Design for Terahertz Quasi-Bound State in the Continuum Biosensors.

ABSTRACT: Terahertz metasurface biosensors based on the quasi-bound state in the continuum (QBIC) offer label-free, rapid, and ultrasensitive biomedical detection. Recent advances in deep learning facilitate efficient, fast, and customized design of such metasurfaces. However, prior approaches primarily establish one-to-one mappings between structure and optical response, neglecting the trade-offs among key performance indicators. This study proposes a pioneering method leveraging transfer learning to optimize multiple indicators in metasurface biosensor design. For the first time, multiple-indicator comprehensive optimization of the quality (Q) factor, figure of merit (FoM), and effective sensing area (ESA) is achieved. The two-stage transfer learning method pre-trains on low-dimensional datasets t

SUBMITTER: Wang S 

PROVIDER: S-EPMC12279224 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature