Millimeter Wave Chipless RFID Authentication Based on Spatial Diversity and 2-D Classification Approach

In this article, a millimeter-wave (mmWave) chipless radio-frequency identification (RFID) tag is developed to operate in the V-band for authentication applications. A novel approach based on tag backscattered E-field measurements at different orientation angles for unitary classification is proposed. The concept is based on the hardness to identically reproduce materials due to the inherent randomness in the fabrication process. These uncertainties are transcribed in very small variations that can be observed in the tag electromagnetic response. A set of 16 identical tags were fabricated, and each tag shares the same fabrication mask and manufacturing process method. Spatial diversity using the tag backscattering pattern (at two different angles) adds independent characteristics for estimating authenticity of each tag. To better exploit the large amount of data collected with this approach, a machine learning (ML) sighting classification is used, which enhances the system performance. The probability of error (PE) achieved with the method is around 1%. This PE is four times lower than the one obtained with a similar approach implemented in the X-band.