Wang Shengfeng, Liu Bingwei, Wu Xu, Jin Zuanming, Zhu Yiming, Zhang Linjie, Peng Yan
Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Shanghai Institute of Intelligent Science and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, Shanghai, 200093, China.
Shanghai Institute of Intelligent Science and Technology, Tongji University, 1239 Siping Road, Shanghai, Shanghai, 200092, China.
Adv Sci (Weinh). 2025 Apr 27:e2504855. doi: 10.1002/advs.202504855.
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 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.
基于连续谱中的准束缚态（QBIC）的太赫兹超表面生物传感器提供了无标记、快速且超灵敏的生物医学检测。深度学习的最新进展促进了此类超表面的高效、快速和定制设计。然而，先前的方法主要在结构和光学响应之间建立一对一映射，而忽略了关键性能指标之间的权衡。本研究提出了一种开创性的方法，利用迁移学习来优化超表面生物传感器设计中的多个指标。首次实现了品质因数（Q）、品质因数（FoM）和有效传感面积（ESA）的多指标综合优化。两阶段迁移学习方法在低维数据集上进行预训练以提取共享特征，然后在复杂的高维任务上进行微调。通过采用频移作为统一标准，这些指标的贡献率被量化为：品质因数为26.09%，品质因数为48.42%，有效传感面积为25.49%。与传统的深度学习方法相比，该方法将数据需求减少了50%。使用该方法设计的生物传感器检测生物标志物同型半胱氨酸，实现了纳克/微升水平的检测，实验结果与理论预测紧密匹配。这项工作为超表面生物传感器设计建立了一种新范式，为痕量生物检测的变革性进展铺平了道路。
