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 to extract shared features, followed by fine‐tuning on complex, high‐dimensional tasks. By adopting frequency shift as a unified criterion, the contribution ratios of these indicators are quantified as 26.09% for the Q factor, 48.42% for FoM, and 25.49% for ESA. Compared to conventional deep‐learning approaches, the proposed method reduces data requirements by 50%. The biosensor designed using this method detects the biomarker homocysteine, achieving detection at the ng µL −1 level, with experimental results closely matching theoretical predictions. This work establishes a novel paradigm for metasurface biosensor design, paving the way for transformative advances in trace biological detection.