ABSTRACT
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.
