The field of flexible antennas is witnessing an exponential growth due to the demand for wearable devices, Internet of Things (IoT) framework, point of care devices, personalized medicine platform, 5G technology, wireless sensor networks, and communication devices with a smaller form factor to name a few. The choice of non-rigid antennas is application specific and depends on the type of substrate, materials used, processing techniques, antenna performance, and the surrounding environment. There are numerous design innovations, new materials and material properties, intriguing fabrication methods, and niche applications. This review article focuses on the need for flexible antennas, materials, and processes used for fabricating the antennas, various material properties influencing antenna performance, and specific biomedical applications accompanied by the design considerations. After a comprehensive treatment of the above-mentioned topics, the article will focus on inherent challenges and future prospects of flexible antennas. Finally, an insight into the application of flexible antenna on future wireless solutions is discussed.

As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

In this article, the optimization of printing properties on a new, flexible ceramic substrate is reported for sensing and antenna applications encompassing internet of things (IoT) devices. E-Strate^® is a commercially available, non-rigid, thin ceramic substrate for implementing in room temperature and high-temperature devices. In this substrate, the printing parameters like drop spacing, number of printed layers, sintering temperature, and sintering time were varied to ensure an electrically conductive and repeatable pattern. The test patterns were printed using silver nanoparticle ink and a Dimatix 2831 inkjet printer. Electrical conductivity, high-temperature tolerance, bending, and adhesion were investigated on the printed samples. The three-factor factorial design analysis showed that the number of printed layers, sintering temperature, sintering time, and their interactions were significant factors affecting electrical conductivity. The optimum printing parameters for the thin E-Strate^® substrate were found to be 20 μm drop spacing, three layers of printing, and 300 °C sintering temperature for 30 min. The high-temperature tolerance test indicated a stable pattern without any electrical degradation. Repetitive bending, adhesion test, and ASTM tape tests showed adequate mechanical stability of the pattern. These results will provide insight for investigators interested in fabricating new IoT devices.