Terahertz metamaterial liquid detector optimized by deep learning

基于深度学习的太赫兹超材料液体探测器

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Abstract

This study proposes a label-free ethanol liquid detection method based on the absorption peak shift of a metamaterial terahertz detector. The designed metamaterial liquid detector adopts a five-layer structure comprising interdigitated copper metal wires, a polyimide dielectric layer, nested double-letter-shaped VO(2) open-loop square rings, and a copper metal plate. Structural parameters were optimized using a deep neural network (DNN), achieving high absorption rates and displacement values under various operating conditions. The detector exhibits a sensitivity of 3.33 THz/RIU and a figure of merit (FOM) of 66 RIU⁻¹. CST simulations reveal that the absorption peak shifts toward the infrared end as droplet volume increases, toward the blue light end as ethanol concentration rises, and the peak spacing narrows under elevated temperatures. Analysis of magnetic field strength and surface current reveals the operating mechanism: At room temperature, VO₂ plays a negligible role in absorption wave regulation, but its enhanced conductivity at elevated temperatures provides dual assurance for ethanol detection accuracy. This study not only offers an efficient and precise new method for ethanol liquid detection but also provides a reference for future applications in food safety, environmental monitoring, and biomedicine.

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