In recent years, there has been great interest in chipless radio-frequency identification (RFID) devices that work in the terahertz (THz) frequency range. Despite advances in RFID technology, its practical use in the THz range has yet to be realized, due to cost and detection accuracy issues associated with shielding materials. In this study, we propose two types of low-cost THz-tags; one is based on the thickness variation of coated polyethylene and the other on the fingerprint spectra of reagents. In the proposed approach, machine learning, specifically a deep-learning method, is used for high-precision tag identification even with weak signals, or when the spectrum is disturbed by passing through shielding materials. We achieved almost 100% identification accuracy despite using an inexpensive tag placed under thick shielding materials with an attenuation rate of about -50 dB. Furthermore, real-time tag identification was demonstrated by combining a multiwavelength injection-seeded THz parametric generator and a convolutional neural network.

中文翻译：

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太赫兹标签可通过使用机器学习的屏蔽材料来识别。

近年来，人们对在太赫兹（THz）频率范围内工作的无芯片射频识别（RFID）设备产生了极大的兴趣。尽管RFID技术取得了进步，但由于与屏蔽材料相关的成本和检测精度问题，其在THz范围内的实际应用尚未实现。在这项研究中，我们提出了两种类型的低成本太赫兹标签：一种基于涂层聚乙烯的厚度变化，另一种基于试剂的指纹图谱。在提出的方法中，即使在信号较弱的情况下，或者当光谱由于穿过屏蔽材料而受到干扰时，机器学习（尤其是深度学习方法）也可用于高精度标签识别。尽管使用了便宜的标签放置在厚屏蔽材料下，衰减率约为-50 dB，但我们仍实现了近100％的识别精度。此外，通过结合多波长注入种子的太赫兹参数发生器和卷积神经网络证明了实时标签识别。