The Growing demands for humidity detection in commercial and industrial applications led to the rapid development of humidity sensors based on different techniques. Surface acoustic wave (SAW) technology is one of these methods that has been found to provide a powerful platform for humidity sensing owing to its intrinsic features, including small size, high sensitivity, and simple operational mechanism. Similar to other techniques, the principle of humidity sensing in SAW devices is also realized by an overlaid sensitive film, which serves as the core element whose interaction with water molecules is responsible for overall performance. Therefore, most researchers are focused on exploring different sensing materials to achieve optimum performance characteristics. This article reviews sensing materials used to develop SAW humidity sensors and their responses based on theoretical aspects and experimental outcomes. Herein the influence of overlaid sensing film on the performance parameters of the SAW device, such as quality factor, signal amplitude, insertion loss, etc., is also highlighted. Lastly, a recommendation to minimize the significant change in device characteristics is presented, which we believe will be a good step for the future development of SAW humidity sensors.

This work presents the sensitivity enhancement of AlN based surface acoustic wave (SAW) pressure sensor fabricated on the Si (100) wafer. Based on the theoretical analysis, a novel concept is proposed to improve pressure sensitivity with crystalline direction. The effect of wave propagation along < 100> and < 110> crystalline direction on pressure sensitivity of the SAW sensor has been studied by the finite element method. The simulation results indicate that the Si (100) based SAW sensor oriented in < 110> direction shown to possess high sensitivity compared to < 100> direction. The increase in sensitivity along the < 110> direction is due to a larger $\gamma $ (velocity sensitivity to strain coefficient) value that results in increased velocity and large frequency shift. Two SAW pressure sensors, PS $_{< 100>}$ and PS $_{< 110>}$ with wave propagation along < 100> and < 110> directions, respectively, have been fabricated on Si (100) wafer to verify the sensitivity enhancement concept. Both sensors have been tested for the pressure range from 0–2 MPa. The measured sensitivity for the PS $_{< 100>}$ and PS $_{< 110>}$ are obtained as 112.68 ppm/MPa and 203.83 ppm/MPa, respectively, Which is significantly improved by 80%. Additionally, the repeatability and linearity of PS $_{< 110>}$ have been measured and the deviation of the measured pressure sensitivity curve from the best linear fit is very small. The obtained results show that the developed sensor design has sufficiently high sensitivity with linear response and can be used for high pressure sensing applications.

This work illustrates the temperature stability of an AlN-based surface acoustic wave (SAW) pressure sensor working at different pressures. At atmospheric pressure, the temperature coefficient of frequency (TCF) in the temperature range of 18°C to 460°C is tested to be -35.68ppm/°C. With increasing pressure from 0MPa to 2MPa, the |TCF| is found as decreased by 0.43ppm/°C. A finite element method (FEM) has been used to investigate the phenomena of decreasing |TCF| and it was found that elastic modulus and thermal mismatch are two main factors contributing to the decrease in |TCF|. At atmospheric pressure, the TCF has been simulated as -17.27ppm/°C, when only the elastic modulus effect was considered. With the increase in pressure from 0MPa to 2MPa, the |TCF| is reduced only by 0.003 ppm/°C, which is very small than experimental findings as strain increases only with the pressure. After including the thermal mismatch between the films, the TCF of the sensor at atmospheric pressure is simulated as -26.50ppm/°C. The |TCF| is decreased by 0.49ppm/°C with increasing pressure from 0MPa to 2MPa, as the strain increases with both pressure and temperature. The sensor’s |TCF| decreases with pressure because the frequency decreases with temperature but increases with pressure, which means that pressure can cancel part of the frequency drop caused by the same temperature range, so the |TCF| at high pressure is smaller.

This work proposes a molybdenum (Mo) based pressure sensor capable of operating at temperatures higher than 300 °C. The sensor utilizes a 30 μm thick rectangular pressure-sensitive film and 2 μm thick gate Mo resistors to form a Wheatstone bridge structure to enhance pressure sensing. Finite element analysis (FEA) has been performed to design the sensor and analyze the pressure and temperature effect. The developed sensor with the excitation of 1 V voltage exhibits excellent performance characteristics at room temperature (25 °C), including a sensitivity of 0.05689 mV V⁻¹ kPa⁻¹, 0.15% full scale (FS) accuracy, a nonlinearity of 0.79% FS, and 0.02% FS repeatability. The thermal zero shift of −0.13% FS/°C is obtained in the 25 °C to 300 °C temperature range. The simulation findings illustrate that the thermal mismatch weakens the thermal zero shift by +0.012‰ FS/°C, while the deviation of the temperature coefficient of resistance (TCR) and initial resistivity ( ρ T 0 ) aggravate the thermal zero shift by −0.139% FS/°C. Among both, the deviation of ρ T 0 and TCR is the major contributor to thermal zero shift. Moreover, it was found that the Seebeck effect caused by the on-chip temperature gradient also affects the accuracy of the sensor. A temperature difference of 5.4 °C produces a thermoelectric potential of 37.95 μV equivalent to a pressure of 670 Pa loading on the chip. Besides, the response characteristics of the sensor at different temperature rates were investigated.

Aeroengine is a vital component of the aircraft, and its health monitoring is considered a critical aspect. Electrostatic (ES) sensors provide a feasible manner to assess the online health monitoring (OHM) of aeroengines by real-time evaluating exhaust emissions. Effective performance prediction of ES sensors is desired in the research and development of ES sensors for OHM systems. Various theoretical and numerical studies have been carried out to model and analyze the ES sensing process. However, the existing models are oversimplified for practical circumstances. There is a crucial need for an advanced model that can predict the performance of the ES sensors under realistic conditions, which is not yet available. To this end, an effort is presented to model and evaluate the performance of the ES sensors. The physical processes involved in ES sensing are analyzed, and the underlying interactions among different physical processes are investigated. Afterward, an advanced multiphysics coupling model is established, reflecting the practical phenomena that have profound effects on ES sensing, including the random release of the particles, temperature-dependent particle charge, nonuniformity of particle trajectories caused by high-speed exhaust gas, and complicated gas path geometry. By reasonably predicting the response of an ES sensor under practical conditions, the proposed model provides a feasible manner to obtain a comprehensive understanding of how design parameters, inherent parameters of particles, and application conditions influence the performance of the ES sensor. Thereby, the developed advanced model facilitates the processes of research and development in the field of sensors.

This work presents and validates an eye-tracking-based visual system for driving the delta robot. A delta robot is tracked by image processing based on vision servo control. The vision servo program is developed in C++ to perform image processing-based object detection. For image processing, Haar classifier-based methods are used. Finally, image processing and motion controller movements are integrated into one system to perform the visual servo-based motion of the end effector of the delta robot. Experiments are performed to validate the proposed method from the perspective of image processing. Moreover, this paper validates the kinematic analysis, which is vital for obtaining 3D information on the end-effector of the delta robot. The presented model can be implemented in eye clinics to facilitate ophthalmologists by replacing manual eye-checking equipment with automatic, unattended, computerized eye checkups.