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In recent years, inkjet printing has emerged as a promising advanced fabrication technology in the field of electronics, offering remarkable advantages in terms of cost-effectiveness, design flexibility, and rapid prototyping. For these reasons, inkjet printing technology has been widely adopted in various applications, including printed circuit board fabrication, sensor development (e.g., temperature, humidity, and pressure sensing), and antenna and filter production, up to the microwave frequency range. The present paper is focused on the investigation of a methodology based on Monte Carlo simulations for quantitatively assessing the influence of fabrication tolerances on the performance of inkjet-printed microwave devices. In particular, the proposed methodology is applied to an inkjet-printed hairpin band pass filter specifically tailored for operation in the L band (i.e., from 1 GHz to 2 GHz). The initial design phase involved the use of computer aided design (CAD) software to optimize the geometric dimensions of the designed filter to closely match the desired performance specifications in terms of bandwidth, insertion loss, and return loss. Later, a Monte Carlo analysis was conducted to evaluate the propagation of tolerances in the fabrication process throughout the design and to estimate their effects on device performance. The fabrication process exploited the advanced capabilities of the Voltera inkjet printer, which was used to deposit a silver-based conductive ink on a commercial Rogers substrate. The device’s performance was evaluated by comparing the simulated scattering parameters with those measured on the developed filter using a vector network analyzer (VNA), thus ensuring accurate validation of real-world performance.

In this work, a SAW resonator is characterized in terms of admittance (Y-) parameters in the temperature range spanning from 0 °C to 100 °C, with the aim of highlighting how its physical properties are affected by the temperature change. A lumped-element equivalent-circuit model is used to represent the device under test at the considered temperature conditions and a parameters extraction process based on a Lorentzian fitting is developed for the determination of the equivalent-circuit elements in the investigated temperature range. A very good agreement is observed between the performed measurements and the model simulations. The characterization process and the subsequent equivalent-circuit parameters extraction at different temperature values are described and discussed.

Nowadays, surface acoustic wave (SAW) resonators are attracting growing attention, owing to their widespread applications in various engineering fields, such as electronic, telecommunication, automotive, chemical, and biomedical engineering. A thorough assessment of SAW performance is a key task for bridging the gap between commercial SAW devices and practical applications. To contribute to the accomplishment of this crucial task, the present paper reports the findings of a new comparative study that is based on the performance evaluation of different commercial SAW resonators by using scattering (S-) parameter measurements coupled with a Lorentzian fitting and an accurate modelling technique for the straightforward extraction of a lumped-element equivalent-circuit representation. The developed investigation thus provides ease and reliability when choosing the appropriate commercial device, depending on the requirements and constraints of the given sensing application. This paper deals with the performance evaluation of commercial surface acoustic wave (SAW) resonators by means of scattering (S-) parameter measurements and an equivalent-circuit model extracted using a reliable modeling procedure. The studied devices are four TO-39 packaged two-port resonators with different nominal operating frequencies: 418.05, 423.22, 433.92, and 915 MHz. The S-parameter characterization was performed locally around the resonant frequencies of the tested SAW resonators by using an 8753ES Agilent vector network analyzer (VNA) and a home-made calibration kit. The reported measurement-based study has allowed for the development of a comprehensive and detailed comparative analysis of the performance of the investigated SAW devices. The characterization and modelling procedures are fully automated with a user-friendly graphical user interface (GUI) developed in the Python environment, thereby making the experimental analysis faster and more efficient.

The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO2). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters.

We study the problem of data gathering in wireless sensor networks and compare several approaches belonging to different research fields; in particular, signal processing, compressive sensing, information theory, and networking related data gathering techniques are investigated. Specifically, we derived a simple analytical model able to predict the energy efficiency and reliability of different data gathering techniques. Moreover, we carry out simulations to validate our model and to compare the effectiveness of the above schemes by systematically sampling the parameter space (i.e., number of nodes, transmission range, and sparsity). Our simulation and analytical results show that there is no best data gathering technique for all possible applications and that the trade-off between energy consumptions and reliability could drive the choice of the data gathering technique to be used. In this context, our model could be a useful tool.