Etienne Perret

This study addresses the optimization of chipless RFID tags for millimeter-wave (mmWave) applications, focusing on the trade-offs among cost-effectiveness and electromagnetic (EM) performance in higher-order (HO) resonant mode operation. It introduces an innovative design strategy that utilizes the HO mode to address both fabrication and electromagnetic constraints in V-band operation. HO tags allow for the relaxation of fabrication constraints related to geometrical limitations and reduce losses considering the thickness of thin laminates in mmWave band operation. By increasing the tag's structural size, the approach uses HO resonant modes to offer a viable solution in V-band operation for numerous applications where fabrication is compatible with a digital printer. Printing compatible with a roll-to-roll approach, where a PET film is printed on both sides with a thermal transfer printer that deposits an extremely thin conductive film to create the resonators. An analysis using the Singular Expansion Method (SEM) is conducted to evaluate the performance of HO resonant tags, emphasizing the individual contributions of fundamental and HO modes. Results show HO modes are more robust to manufacturing imperfections, with smaller fractional frequency shifts than fundamental-mode designs, and offer threefold higher spectral efficiency. The tags are produced on a cost-effective substrate, enabling a print-and-use approach suitable for a wide range of applications.

This research paper introduces a system engineered to detect hand gestures utilizing a depolarizing chipless RFID tag held by an individual positioned in front of a monostatic radar. The system ascertains the location of the chipless RFID tag within a three-dimensional space by employing the backscattered phase from each resonator in conjunction with multilateration. Through the assessment of the tag’s location in three-dimensional space, a range of hand gestures can be recognized employing uncomplicated processing techniques based on the similarity metric of turning distances. This renders the proposed technique suitable for real-time applications, eliminating the necessity for machine learning or training. The system was evaluated by detecting six basic hand gestures while the tag was affixed to a Styrofoam arm positioned on a 3D position table and held in a human hand within an office environment. Statistical analyses showed that the proposed gesture recognition works well within a 3D volume of 10 × 10 × 10 cm³. The suggested gesture recognition system shows promise for real-world applications, as it may function effectively even when the reader antenna is concealed beneath materials such as wood, gypsum, and chipboard. This paper is accompanied by two videos demonstrating the proposed gesture recognition system.

This work presents a bit-wise chipless radio frequency identification (RFID) tag detection framework based on convolutional neural networks (CNNs). The proposed framework demonstrates two key capabilities: first, unlike previously proposed models, it allows decoding tag identifiers not used during the training phase of the models, making it possible to introduce a decoding method that could potentially be applied to detect high-capacity chipless tags; and second, it illustrates how synthetic data can be used to train models that generalize to real measurements, showing how instead of measuring each tag individually to construct a training dataset, data can instead be synthetically generated based on the knowledge of the tags’ design. Moreover, the framework includes a correction process to manage invalid predictions (i.e., those that are not part of the set of considered tags) and a probabilistic-based filtering process to identify uncertain predictions. Together, these strategies result in more confident predictions and fewer errors overall, showcasing the utility of error-correction strategies and the use of “soft” classifiers. The framework is assessed using different datasets for training: a dataset obtained from the recordings of S21 of wireless frequency-coded chipless RFID tags in a monostatic configuration, a synthetic dataset generated by modeling resonators as second-order bandpass filters, and a dataset made of the two previous datasets. All models are then employed to predict measurements made in a real-world setup. In the interval 20–50 cm, the model trained with real measurements achieves an accuracy of 99% after filtering, with a predictions conserved ratio (PCR) of 0.62; the one trained with synthetic measurements achieves an accuracy of 98% with a PCR of 0.61; and the one trained with a combination of real and synthetic measurements leads to an accuracy of 97% with a PCR of 0.70. If the distance is decreased (e.g., to 20–30 cm), higher accuracies and PCR values are obtained, as expected, achieving more than 99% accuracy after filtering in all cases. Overall, these results show how the proposed framework can correctly predict tags not seen during training, and how synthetic data can be used to train the models without harming their performance, while at the same time decreasing the time of measurement collection.

The rise of the Internet of Things (IoT) is creating new communication needs that require us to completely rethink how we design wireless interaction between potentially diverse devices, which must be increasingly environmentally friendly and agile. This article introduces a wireless communication method elegantly grounded in the principle of the RF feedback loop. Unlike traditional communication methods, this approach does not require the reader to emit any waves in the absence of a transponder. Instead, communication occurs when a self-sustaining wave spontaneously emerges as the transponder enters the reader’s reading zone, forming an active feedback loop between the two devices. This novel method of transmitting RF signals creates a new paradigm in the communications field that will significantly influence modes of interaction, especially in the IoT. This method significantly decreases the emission of unnecessary RF waves and ensures complete discretion for the reader when no transponder is within range. The principle of Wireless Active Feedback Loop Communication (WAFLC) has been validated through both simulations and practical tests, showing a strong correlation between the two.

This article presents a real-time identification process for slow motion-modulated chipless radio frequency identification (RFID), which can be applied to human hand gestures to realize a full-practical long-range RFID system with gesture recognition capability. Depolarizing chipless tags configured by hand in three gestures are utilized, while different motion-induced modulation effects involved in each gesture are analytically modeled. The identification based on differential radar cross-section (RCS) and gesture recognition using deterministic in-phase/quadrature (IQ) trajectories is addressed according to the developed model. The fast reading process for slow motion-modulated chipless RFID is proposed based on the sequential fast frequency sweep acquisitions by vector network analyzer (VNA), while the sweep time is very short compared to the period of motion. The identification time achieved using this approach is about only a few periods of motion, which is ideal for implementing hand gesture-modulated chipless RFID in real time. The designed gesture-modulated chipless tag is identified during a few seconds at distances up to 3 m for the most efficient gesture. Moreover, all the hand gestures are recognized at the 2-m range based on the received IQ signature. These achievements realize the practical example of motion-modulated chipless RFID where both tag identification and gesture recognition are simultaneously provided.

A Life Cycle Assessment (LCA) is performed to evaluate the environmental impact of chipless RFID using the standardized LCA method. This allows for a comparison of these impacts with those of UHF RFID and barcode technologies. Identification technologies such as RFID (UHF, HF) are intended to be disseminated worldwide to the greatest number of people and therefore to generate the use of a very large number of tags. Thus, several billion UHF and HF labels are produced per year. Insofar as the lifespan of these labels is short and therefore can be considered disposable components, it is vital to measure their impact on the environment. This is not only due to the growing concerns about global warming but also to explore alternatives to RFID tags that can help conserve resources and reduce pollution. This study allows us to evaluate the environmental impact and carbon footprint. The results of this research will provide new insights and suggest ways to reduce the environmental impact of the electronics industry.

Optical actuation of GeTe-based radio frequency switches is studied for the first time at 915-nm optical wavelength. By inducing self-heating of the phase change material through light absorption, this approach removes the need for integrated micro-heaters. First, laser pulse conditions required to set the RF switches in ON-and OFF-states are found with optical pulse peak powers up to few hundred mW. Amorphization is obtained for optical pulses of 100 to 500 ns, and crystallization for pulses of 2 to 6 μs. A resistivity ratio of 2E4 is measured between both states. Cycling endurance is performed on a 4x5 μm ² switch, overpassing 1500 cycles, which is much higher compared to previously reported optical demonstrations. Small-signal measurements show 10% lower OFF-state capacitances using optical actuation compared to electrical actuation through micro-heaters. 37 dBm power handling capability is reported in the switch OFF-state at 915 MHz. This optical actuation demonstration corresponds to a standard wavelength in integrated photonic circuits and thus, this work constitutes a first step towards a CMOS compatible fully-integrated optical actuation solution for RF switches based on phase-change material.

This paper discusses a bistatic reading configuration that employs a frequency-spatial encoding strategy to enhance the data encoding capacity in chipless radio frequency identification (RFID) systems. The proposed method allows for the estimation of resonant frequencies associated with each scatterer and the determination of relative resonator positions. The approach relies on an imaging algorithm that evaluates the positions of the resonators by considering a single-frequency point corresponding to the resonant frequency of each scatterer. Consequently, the proposed method does not seek to image the shape of the metal scatterers but rather to locate their positions geometrically for different frequencies. Indeed, once the resonance frequencies have been detected, a geometric representation of the backscattered field is created by limiting the observable frequency band to the resonance frequencies. The use of individual processing with single-frequency steps enables the distinction of the side-by-side gap between resonators, even at resolutions below the conventional image resolution. Furthermore, this method has the capability to differentiate the gap spacing, which can be as small as λ/8, between resonators operating at different resonance frequencies. This technique significantly increases the coding capacity associated with the chipless tag. A proof of concept (PoC) is evaluated based on loop resonators for identification purposes. Then, anechoic and real scenario measurements (3D displacement table and conveyor belt) are considered in different acquisition configurations. An office environment reading range of up to 20cm has been reported.

This article introduces multistate (M-S) chipless radio-frequency identification (RFID) tags that can be utilized to enhance the vibration sensing range and reading robustness in real environments. The proposed M-S chipless tag consists of a resonant scatterer trapped between two different dielectric layers, which causes the near-field boundary of the scatterer to be modulated during vibration and results in the appearance of multiple modulation states. The motion-induced near-field modulation provided by the M-S chipless tag is much more effective than the natural Doppler modulation caused by vibration, which significantly increases the modulated portion of the backscattered power, and consequently, enlarges the sensing range. The backscattering from the vibrating M-S chipless tag is studied using presented mechanical and electromagnetic models that are verified by simulations. The designed M-S chipless tag is implemented, and the concept is experimentally proven. The differential radar cross section (RCS) of the vibrating M-S chipless tag is 45 times greater than that of a vibrating classical chipless tag, which translates into an increase of factor 2.5 in the read range. The M-S chipless RFID tag is utilized to successfully detect a submillimeter vibration through a wall in an indoor environment at low microwave frequencies, while the vibrating tag is located at a 2.5-m distance.

In the literature, chipless RF identification (RFID) tags are mostly characterized at normal incidence, where the 3D spherical reading zone of chipless RFID tags has not been characterized. In this paper, a compact measurement system for the characterization of scatterers in 3D spherical read range is presented. Customized support structures are realized for the commercial multi-probe measurement system StarLab from MVG to construct the 3D spherical bistatic and monostatic measurement systems. A graphical user interface (GUI) is programmed to control the measurement system and to perform postprocessing of the measurements. Owing to this measurement setup, 3D characterization of three scattering objects is presented: an aluminum sphere, a nondepolarizing rectangular loop based chipless RFID tag, and a depolarization RF Elementary Particle (REP) chipless RFID tag. The measurement setup is capable to characterize any lightweight scatterer.