Shape-changing mechanical metamaterials have drawn the attention of researchers toward the development of continuous-range tunable frequency selective surfaces (FSSs). In this paper, a novel tunable FSS utilizing an origami-inspired “eggbox” structure is presented featuring four-degrees of freedom that can change the frequency response of two orthogonal linear polarizations. The centrosymmetric “eggbox” structure can be folded or rotated along two axes that lead to unprecedented reconfigurability compared to traditional Miura-Ori-based structures which have fewer degrees of control. The utilized cross-shaped dipole FSS element shows enhanced bandwidth, support for orthogonal linear polarization, and ease of fabrication. The prototype is fabricated using a low-cost fully additive inkjet printing process with silver nanoparticle conductive ink. The outcome of this study shows a 25% frequency tunable range over two polarization directions. The design can be an ideal spatial filtering candidate for advanced ultra-wideband terrestrial and space applications.

This paper presents a polarization-independent 11-bit chipless RFID tag based on frequency-selective surface which has been designed for encoding and relative humidity (RH) sensing applications. The 10 exterior U-shaped resonators are used for item encoding whereas Kapton has been incorporated with the interior resonator for RH sensing. This radio-frequency identification (RFID) tag operates in S- and C-frequency bands. The proposed design offers enhanced fractional bandwidth up to 88% with the density of 4.46 bits/cm2. Both single- and dual-layer tags have been investigated. The simulated results are in good agreement with measured results and a comparison with existing literature is presented to show the performance. Simple geometry, high code density, large frequency signature bandwidth, high magnitude bit, high radar cross-section, and angular stability for more than 75° are the unique outcomes of the proposed design. In addition, RH sensing has been achieved by integrating the Kapton on the same RFID tag.

Specifications of the substrates are among the most important and problematic parameters that still do not have proper models in the design procedures of metasurfaces. In this paper, a new fast and exact algorithm based on artificial neural networks (ANNs) is presented, which makes it possible to design frequency-selective surfaces (FSSs) on various kinds of standard substrates. Also for the first time, designing FSSs on uniaxial anisotropic substrates can be easily done in short time and without any optimization algorithms. During this paper, first equivalent geometry approach (EGA) is demonstrated as a new method of preparation the ANNs. Then EGA is used to train geometry transformation ANNs. The advantage of this approach is to reduce the size of training datasets by about 98% and prevent from superfluous simulations. Hence, the time needed for training of the networks is much less than before. Numerical results are used to show that the required time for developing FSSs is <200 ms on average, and errors are <2%. For the final validation, a prototype sample of FSS is fabricated on the RO4003 substrate with 20 mil thickness. Both analytical and experimental results confirm the correctness of the predicted values.