Explore the vast range of titles available.

Over the last couple of decades, the advancement in Microelectromechanical System (MEMS) devices is highly demanded for integrating the economically miniaturized sensors with fabricating technology. A sensor is a system that detects and responds to multiple physical inputs and converting them into analogue or digital forms. The sensor transforms these variations into a form which can be utilized as a marker to monitor the device variable. MEMS exhibits excellent feasibility in miniaturization sensors due to its small dimension, low power consumption, superior performance, and, batch-fabrication. This article presents the recent developments in standard actuation and sensing mechanisms that can serve MEMS-based devices, which is expected to revolutionize almost many product categories in the current era. The featured principles of actuating, sensing mechanisms and real-life applications have also been discussed. Proper understanding of the actuating and sensing mechanisms for the MEMS-based devices can play a vital role in effective selection for novel and complex application design.

Piezoelectric microelectromechanical system (piezo-MEMS)-based mass sensors including the piezoelectric microcantilevers, surface acoustic waves (SAW), quartz crystal microbalance (QCM), piezoelectric micromachined ultrasonic transducer (PMUT), and film bulk acoustic wave resonators (FBAR) are highlighted as suitable candidates for highly sensitive gas detection application. This paper presents the piezo-MEMS gas sensors’ characteristics such as their miniaturized structure, the capability of integration with readout circuit, and fabrication feasibility using multiuser technologies. The development of the piezoelectric MEMS gas sensors is investigated for the application of low-level concentration gas molecules detection. In this work, the various types of gas sensors based on piezoelectricity are investigated extensively including their operating principle, besides their material parameters as well as the critical design parameters, the device structures, and their sensing materials including the polymers, carbon, metal–organic framework, and graphene.

Diabetes is currently screened invasively by measuring glucose concentration in blood, which is inconvenient. This paper reports a study on modeling and simulation of a CMOS-MEMS sensor for noninvasive screening of diabetes via detection of acetone vapor in exhaled breath (EB). The sensor has two structures: movable (rotor) and fixed (stator) plates. The rotor plate is suspended on top of the stator by support of four flexible beams and maintaining certain selected initial gaps of 5, 6, 7, 8, 9, 10, or 11 μm to form actuation and sensing parallel plate capacitors. A chitosan polymer of varied thicknesses (1–20 μm) is deposited on the rotor plate and modeled as a sensing element for the acetone vapor. The minimum polymer coating thickness required to detect the critical concentration (1.8 ppm) of acetone vapor in the EB of diabetic subjects is found to be 4–7 μm, depending on the initial gap between the rotor and stator plates. However, to achieve sub-ppm detection limit to sense the acetone vapor concentration (0.4–1.1 ppm) in the EB of healthy people, up to 20 μm polymer thickness is coated. The mathematically modeled results were verified using the 2008 CoventorWare simulation software and a good agreement within a 5.3% error was found between the modeled and the simulated frequencies giving more confidence in the predicted results.

Resistive random access memory (RRAM) has two distinct processes, the SET and RESET processes, that control the formation and dissolution of conductive filament, respectively. The laws of thermodynamics state that these processes correspond to the lowest possible level of free energy. In an RRAM device, a high operating voltage causes device degradation, such as bends, cracks, or bubble-like patterns. In this work, we developed a statistical simulation of the switching mechanism in a ZnO-based RRAM. The model used field-driven ion migration and temperature effects to design a ZnO-based RRAM dynamic SET and RESET resistance transition process. We observed that heat transport within the conducting filament generated a great deal of heat energy due to the carrier transport of the constituent dielectric material. The model was implemented using the built-in COMSOL Multiphysics software to address heat transfer, electrostatic, and yield RRAM energy. The heat energy increased with the increase in the operating power. Hence, the reliability of a device with high power consumption cannot be assured. We obtained various carrier heat analyses in 2D images and concluded that developing RRAM devices with low operating currents through material and structure optimization is crucial.

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as “Pure (80 samples)” and “Adulterated (90 samples)”. Lard was mixed with the ratio of 10–50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700–1800 cm−1 and 2800–3000 cm−1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

This paper reports a low-cost, high-sensitivity CMOS-MEMS piezoresistive accelerometer with large proof mass. In the device fabricated using ON Semiconductor 0.5 μm CMOS technology, an inherent CMOS polysilicon thin film is utilized as the piezoresistive sensing material. A full Wheatstone bridge was constructed through easy wiring allowed by the three metal layers in the 0.5 μm CMOS technology. The device fabrication process consisted of a standard CMOS process for sensor configuration, and a deep reactive ion etching (DRIE) based post-CMOS microfabrication for MEMS structure release. A bulk single-crystal silicon (SCS) substrate is included in the proof mass to increase sensor sensitivity. In device design and analysis, the self heating of the polysilicon piezoresistors and its effect to the sensor performance is also discussed. With a low operating power of 1.5 mW, the accelerometer demonstrates a sensitivity of 0.077 mV/g prior to any amplification. Dynamic tests have been conducted with a high-end commercial calibrating accelerometer as reference.

Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal’s energy below the noise floor with no additional hardware requirements and ease of implementation. This research article offers the technical evaluation of the trilateration technique, its efficiency, and its limitations for the localization using LoRa ESP in a large outdoor populated campus environment. Additionally, experimental evaluations are conducted to determine the effects of frequency hopping, outlier removal, and increasing the number of gateways on localization accuracy. Results obtained from the experiment show the importance of calculating the path loss exponent for every frequency to circumvent the high localization error because of the frequency hopping, thus improving the localization performance without the need of using only a single frequency.

The Internet of Things (IoT) domain has experienced significant growth in recent times. There has been extensive research conducted in various areas of IoT, including localization. Localization of Long Range (LoRa) nodes in outdoor environments is an important task for various applications, including asset tracking and precision agriculture. In this research article, a localization approach using Support Vector Regression (SVR) has been implemented to predict the location of the end node using LoRaWAN. The experiments are conducted in the outdoor campus environment. The SVR used the Received Signal Strength Indicator (RSSI) fingerprints to locate the end nodes. The results show that the proposed method can locate the end node with a minimum error of 36.26 meters and a mean error of 171.59 meters.

The microelectromechanical system (MEMS) is one of the most diversified fields of microelectronics; it is rated to be the most promising technology of modern engineering. MEMS can sense, actuate, and integrate mechanical and electromechanical components of micro- and nano sizes on a single silicon substrate using microfabrication techniques. MEMS industry is at the verge of transforming the semiconductor world into MEMS universe, apart from other hindrances; the reliability of these devices is the focal point of recent research. Commercialization is highly dependent on the reliability of these devices. MEMS requires a high level of reliability. Several technological factors, operating conditions, and environmental effects influencing the performances of MEMS devices must be completely understood. This study reviews some of the major reliability issues and failure mechanisms. Specifically, the fatigue in MEMS is a major material reliability issue resulting in structural damage, crack growth, and lifetime measurements of MEMS devices in the light of statistical distribution and fatigue implementation of Paris' law for fatigue crack accumulation under the influence of undesirable operating and environmental conditions.

Design and modeling of a CMOS MEMS device using 0.35 µm CMOS technology is used to achieve high sensitivity on mass sensing is presented in this paper. The purpose of this paper is to investigate the effect of increasing beams lengths which support the membrane of the device, on the resonance frequency to achieve high sensitivity. A study on the effect of added mass on the device on natural frequency is also conducted. Mass sensitivity of this device is found to be 153 mHz/ng. At damping ratio of 0.0002, the resonant frequency of the resonator is 19.04 kHz with quality factor 3500.