首页|期刊导航|测试科学与仪器|基于微观结构的太赫兹传感技术:从电磁响应增强到多尺度检测

基于微观结构的太赫兹传感技术:从电磁响应增强到多尺度检测OA

Microstructure-based terahertz sensing technology:from electromagnetic response enhancement to multi-scale detection

中文摘要英文摘要

太赫兹波具有高透射、强吸收和低光子能量等特性,在生物医学诊断、无损检测以及食品与农产品质量安全监测等领域具有广泛的应用,基于太赫兹波的传感器也因此受到越来越多的关注.然而,传统耦合结构设计与太赫兹波的高频振荡特性难以有效契合,导致信号能量传输效率低下,限制了太赫兹传感器的性能.而微结构技术通过构造亚波长共振单元与太赫兹波高频振荡特性的精确匹配,实现了电磁场能量的局域化增强,显著增强了太赫兹传感器灵敏度.本文总结了部分基于开口环谐振器、光子晶体、波导谐振腔与表面等离子体共振等不同微结构的太赫兹传感器基本原理及研究现状.尤其值得关注的是,近年来人工智能特别是深度学习技术发展迅速,其在信号处理、模式识别与逆向设计等领域的强大能力正逐步渗透至太赫兹传感技术领域.将深度学习算法与太赫兹传感器设计相结合,不仅能够有效挖掘复杂太赫兹信号中隐含的物质特征信息,提升目标识别与分类精度,还可通过对微结构参数空间的智能搜索与优化,辅助甚至自主完成高性能太赫兹传感结构的逆向设计与性能预测.这一跨学科融合趋势为突破传统设计方法的局限性、实现太赫兹传感器性能的跨越式提升开辟了新的路径.

Terahertz(THz)waves exhibit distinctive properties,such as high transmittance,pronounced absorption,and minimal photon energy,enabling a wide range of applications in biomedical diagnosis,non-destructive testing,and quality/safety monitoring of food and agricultural products.Consequently,THz-based sensors have garnered increasing attention.However,the design of traditional coupling structures fails to effectively match the high-frequency oscillation of THz waves,resulting in low signal energy transmission efficiency and limiting the performance of THz sensors,while microstructure technology can offer a solution by achieving localized enhancement of the electromagnetic field energy through precise matching of sub-wavelength resonance units with the high-frequency oscillation of THz waves,which significantly improves the sensitivity of THz sensors.This review summarizes the basic principles and research status of various THz sensors based on different microstructures,such as split-ring resonators(SRRs),photonic crystals,waveguide resonators,and surface plasmon resonance.Notably,the rapid development of artificial intelligence,especially deep learning,is increasingly influencing THz sensing technologies with its strengths in signal processing,pattern recognition accuracy,and inverse design.Integrating deep learning with THz sensor design enhances feature extraction from complex signals,improves target identification,and enables intelligent optimization of microstructure parameters for high-performance sensor design and performance prediction.This interdisciplinary approach provides a new pathway to overcome traditional design limitations and advance THz sensor performance.

张姝;雷程;梁庭;牛文宇;郝亚峰;马富鹏;朱璞;武慧嘉;黄育杰;李腾腾;刘美宏

天津津航技术物理研究所,天津 300308中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051中北大学 极限环境光电动态测试技术与仪器全国重点实验室,山西 太原 030051

太赫兹人工微结构传感器表面等离子体共振波导光子晶体深度学习

terahertz(THz)artificial microstructuressensorssurface plasmon resonancewaveguidesphotonic crystalsdeep learning

《测试科学与仪器》 2026 (1)

1-15,15

This work was supported by National Natural Science Foundation of China(Nos.62301509,62405293),and General Project of China Postdoctoral Science Foundation(No.2025M770537).

10.62756/jmsi.1674-8042.2026001

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