Microelectromechanical Systems Sensors Research Articles

Comprehensive calibration and laboratory validation of a Micro Electromechanical System sensor–based flexible inclinometer

Abstract Real-time monitoring of slopes, tunnels, and dams is important for ensuring the long-term stability and reliability of such structures. Despite the successes of current technologies in many applications, a gap still exists in certain areas such as precision in steep slopes and complex soil conditions. This study has designed a flexible inclinometer based on an array of Micro Electromechanical System (MEMS) sensors to enhance the accuracy and flexibility of existing monitoring techniques. The inclination angle of each flexible inclinometer measurement unit was measured to monitor the horizontal or vertical displacement of the target structural body. We used the Levenberg–Marquardt (LM) algorithm for optimizing the MEMS sensors based calibration and designed multiple experiments to test the accuracy of the proposed method. Experimental results show that the calibrated flexible inclinometer measurement unit has an inclination angle of less than 0.04° and the accuracy of the flexible inclinometer lies within ±0.4 mm in the horizontal attitude and 1.6 mm in the vertical attitude. Our research has developed a novel tool for geotechnical engineering monitoring that can aid in increasing the precision of real-time assessment and prediction of structural stability.

Oxygen-termination effect on the surface energy dissipation in diamond MEMS

Single-crystal diamond (SCD) microelectromechanical systems (MEMS) resonators with lower energy dissipation and higher quality (Q) factors have always been pursued for the development of high-sensitivity and high signal-to-noise ratio (SNR) MEMS sensors. The intrinsic loss such as the crystal quality and extrinsic loss of clamping loss have been examined to improve the Q factors of SCD MEMS resonators. Nevertheless, the surface termination induced energy dissipation has rarely been known due to the lack of high crystal quality diamond resonators and in-situ characterization. Here we examine the effect of oxygen-termination on the surface energy dissipation of SCD cantilevers by in-situ heating these cantilevers in a high vacuum chamber. After thermal treatment of the cantilevers at 933 K, the Q factor of the cantilever is improved from 2.8x105 to 3.3x105 (i.e. the 120 μm-long). The resonance frequency increase confirms the desorption of surface adsorbates. Compared to silicon, on which a native solid-state oxide exists, the surface oxygen-termination induced loss in diamond MEMS is much smaller. The non-existence of native oxides on diamond surface is an obvious merit toward ultra-high Q factor MEMS resonators.

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period.

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive measurements even if, in the latest years, invasive microelectromechanical systems (MEMS) sensors have emerged as a valuable approach for precise and continuous monitoring of cardiac activity. The main challenges in designing cardiac MEMS sensors are represented by miniaturization, biocompatibility, and long-term stability. Here, we present a MEMS piezoresistive cardiac sensor capable of continuous monitoring of LV activity over time following epicardial implantation with a pericardial patch graft in adult minipigs. In acute and chronic scenarios, the sensor was able to compute heart rate with a root mean square error lower than 2 BPM. Early after up to 1 month of implantation, the device was able to record the heart activity during the most important phases of the cardiac cycle (systole and diastole peaks). The sensor signal waveform, in addition, closely reflected the typical waveforms of pressure signal obtained via intraventricular catheters, offering a safer alternative to heart catheterization. Furthermore, histological analysis of the LV implantation site following sensor retrieval revealed no evidence of myocardial fibrosis. Our results suggest that the epicardial LV implantation of an MEMS sensor is a suitable and reliable approach for direct continuous monitoring of cardiac activity. This work envisions the use of this sensor as a cardiac sensing device in closed-loop applications for patients undergoing heart surgery.

Graphene‐Reinforced Hybrid Polymer Nanocomposites‐Based Biosensor Implementation for Environmental Remediation

AbstractBiosensor which is used in wide range of research areas is a type of device which analyzes chemical and biological reactions by responding with electrical output. In this process graphene has become a “miracle material” due to its chemical and physical properties and it is synthesized in this process by native graphite powder (Black Lead) with slight moderation of Hummers method. For membrane formation, PVB (polyvinyle butryl) is used. This copolymer is totally amorphous in nature. An additional, most important eminence of polymer based composite materials is that they can modify chemically to enhance their elasticity biocompatibility and reactivity. The novel nanocomposite membrane can be used for photocatalytic materials for organic pollutant degradation. This experimental study is focused on synthesis and catalytic activity of electrochemical biosensing polymer nanocomposites. In fact, there are many other fields where graphene‐enhanced sensors are on the rise, such as microelectromechanical systems (MEMS) sensors, pressure sensors, pH sensors, environmental contamination sensors, gas sensors, DNA sensors, and more.

Wireless Blackbox for Cars Using Sensors and GPS Module

Abstract: The main purpose of this wireless black box project is to develop a vehicle black box system that can be installed into any vehicle all over the world. This paradigm is often designed with minimum range of circuits. Wireless black box is basically a device that will indicate all the parameters of a vehicle crash and will also store and display its parameters such as temperature, location, vibration, alcohol limit etc. At the time of accident, the message will be sent from the system built inside the car to the registered mobile numbers such as emergency numbers of police stations, hospitals, family members, owner etc. We have used various types of sensors like temperature sensor, which is used to measure temperature. Vibration sensor measures vibrations felt by the car during accident. Alcohol sensor is located on the steering wheel which will indicate whether the driver is drunk. Micro-electromechanical system sensor is used to indicate tilt during the accident. GSM module, GPS module are some of the devices used in this project which helps in accomplishing the output.

Hierarchically porous ZnO derived from zeolitic imidazolate frameworks for high-sensitive MEMS NO2 sensor

Three-dimensional (3D) porous metal oxide nanomaterials with controllable morphology and well-defined pore size have attracted extensive attention in the field of gas sensing. Herein, hierarchically porous ZnO-450 was obtained simply by annealing Zeolitic Imidazolate Frameworks (ZIF-90) microcrystals at an optimal temperature of 450 °C, and the effect of annealing temperature on the formation of porous nanostructure was discussed. Then the as-obtained ZnO-450 was employed as sensing materials to construct a Micro-Electro-Mechanical System (MEMS) gas sensor for detecting NO2. The MEMS sensor based on ZnO-450 displays the excellent gas-sensing performances at a lower working temperature (190 °C), such as high response value (242.18% @ 10 ppm), fast response/recovery time (9/26 s) and ultralow limit of detection (35 ppb). The ZnO-450 sensor shows better sensing performance for NO2 detection than ZnO-based composites materials or commercial ZnO nanoparticles (NPs), which are attributed to its unique hierarchically structures with high porosity and larger surface area. This ZIFs driven strategy can be expected to pave a new pathway for the design of high-performance NO2 sensors.

Inkjet-printed MOS-based MEMS sensor array combined with one-dimensional convolutional neural network algorithm for identifying indoor harmful gases

To achieve a highly accurate and efficient analysis of mixed gases, it is crucial to develop an electronic nose system with high sensitivity of gas sensors and low data processing complexity. In this study, the metal oxide semiconductor (MOS) based micro-electromechanical system (MEMS) gas sensor array was prepared by inkjet printing sensing materials onto a micro-hotplate. The pattern recognition unit employed a one-dimensional convolutional neural network (1D-CNN) to identify 7 types of gases. While the optimal traditional machine learning algorithm achieved an 80% recognition accuracy, the 1D-CNN can achieve 99.8%. Furthermore, the impact of varying time series input lengths on the model accuracy was investigated, pinpointing an optimal sampling time of 15 s. This work showed that integrating the MEMS sensor array with the 1D-CNN algorithm might offer a promising approach for intricate gas classification and identification.

Theoretical analysis and comparative study of assorted diaphragm primarily based Micro Electro Mechanical System (MEMS) optical pressure sensors

In general, micro pressure sensors use a diaphragm as the sensing element that converts the applied pressure into the required displacement. Displacement is employed to sense real physical phenomena, such as pressure, force with various conduction methods. The conduction is defined as capacitive, piezoresistive, piezoelectric or resonant. Understanding the behavior of structural elements prior to its design is very essential depends on conductive method. The main challenge of parameter optimization of the diaphragm is to attain unsurpassed device performance. Therefore, theoretical analysis of various structures of optical Micro Electro Mechanical System (MEMS) sensor is proposed in this manuscript. Here, diaphragm geometries, like square, rectangular and circular are considered to analyze the structures. The objective of this work is to analyze various structures of optical MEMS sensor measuring pressure theoretically. The displacements of the varied diaphragms are compared for different thicknesses and the graphs are analyzed against their displacements. The best suitable structure of diaphragm for pressure sensor design is obtained. The diaphragm with good characteristics is virtually fabricated using Intellifab. The parameters such as linearity and relationship between pressure and intensity are obtained for the best suited diaphragm.

Triaxial tactile sensor utilizing standing laser-induced graphene cantilevers on polyimide film

Microelectromechanical systems (MEMS) multiaxial tactile sensors have been developed in various fields to leverage their small size, high accuracy, and high sensitivity. Although various measurement principles have been proposed, most tactile sensors are fabricated using conventional and complicated Si processes. Conversely, laser-induced graphene (LIG) has recently attracted significant attention as a material for fabricating physical sensors due to its high sensitivity as a piezoresistor through simple laser processing. Therefore, the application of LIG to MEMS processes is expected to yield unprecedented MEMS sensor devices. In this paper, we propose a triaxial tactile sensor using standing LIG cantilevers embedded in an elastic body. The proposed sensing element was fabricated using ultraviolet (UV) laser irradiation to shape a polyimide film into cantilever structures and CO2 laser irradiation to form LIG strain gauges. Subsequently, the flat LIG cantilevers were stood up using thermal deformation. These multiple three-dimensional LIG cantilever elements enable multiaxial force measurements. The cantilever and elastic body portion of the sensor had dimensions of 3 × 3 × 0.2 mm and 12.5 × 12.5 × 3 mm, respectively. The fabricated sensor response was evaluated, and its capability to independently measure triaxial forces was verified. Therefore, the proposed simple and reproducible fabrication process is useful for realizing compact multiaxial tactile sensors.

MEMS fault-tolerant machine learning algorithm assisted attitude estimation for fixed-wing UAVs

The utilization of Micro-electromechanical Systems (MEMS) sensors is widespread for directly detecting attitude angles, such as Accelerometer, Gyro, and Magnetometer readings. However, these MEMS sensors are prone to flaws, leading to inaccurate estimates of attitude angles and, consequently, causing UAVs to lose control. Given that UAVs are operated remotely over long distances, ensuring accurate attitude estimates becomes crucial. This study aims to address this issue by employing machine learning algorithms (MLA). These algorithms were trained and evaluated to overcome the problem by predicting missing data from a malfunctioning MEMS sensor using the available data from other MEMS sensors. To calculate the attitude angles, the study utilizes the Extended Kalman Filter (EKF) technique. Furthermore, a novel fault-tolerant machine learning-aided estimation algorithm has been proposed specifically for estimating the attitude angles (φˆ, θˆ, ψˆ) of fixed-wing UAVs. The significance of this research becomes even more prominent when considering the occurrence of MEMS sensor failure. In such cases, the machine learning algorithm plays a crucial role as it has been pre-trained specifically to handle these scenarios. The algorithm is equipped with the ability to effectively address and mitigate the challenges posed by MEMS sensor failures. By leveraging its pre-existing knowledge and learned patterns, the algorithm can accurately predict missing data caused by malfunctioning MEMS sensors. This capability proves invaluable in ensuring the reliable estimation of attitude angles, even in the face of sensor failures. Thus, the integration of machine learning into the estimation process enhances the resilience and robustness of the system, making it well-equipped to handle the uncertainties introduced by MEMS sensor failures.

Optical Interferometric MEMS Accelerometers

AbstractLaser interferometry is one of the most accurate measurement technologies whose displacement resolution can reach the femtometer (fm) level. With the development of Micro‐Electro‐Mechanical Systems(MEMS) technology, many excellent optical interferometric MEMS sensors have emerged, which play an essential role in intelligent manufacturing, aerospace, and many other fields. Among them, optical interferometric MEMS accelerometers develop rapidly due to their high sensitivity, low noise, and anti‐electromagnetic interference advantages. Lots of research in optical interferometric chip design, micro‐nano fabrication process, signal demodulation algorithm, and range extension technology are performed, and different types of MEMS accelerometers based on the optical interferometric principles are developed, which have been demonstrated and applied in the fields of disaster monitoring, equipment operation and maintenance, and resource exploration. This paper reviews the development status of optical interferometric MEMS accelerometers, mainly focusing on the critical problems and solutions that need to be solved in the development process of optical interferometric accelerometers, and finally introduces the typical application scenarios.

An Autoencoder‐Based Deep‐Learning Method for Augmenting the Sensing Capability of Piezoelectric Microelectromechanical System Sensors in a Fluid‐Dynamic System

Herein, an innovative deep‐learning architecture is proposed to enhance the sensing capabilities of a microelectromechanical system (MEMS) used in fluid dynamic applications. The MEMS sensor comprises a polyvinylidene fluoride flexible (PVDF) piezoelectric flag and a bluff body, with vortex generation influenced not only by the bluff body's geometry but also by the input fluid speed. As a result, mechanical vibrations are induced in the piezoelectric flag, leading to charge displacement and the generation of electrical voltage signals. Through the developed deep learning method, accurate extraction of wind speed and successful classification of turbulence are achieved. Experimental tests in a wind tunnel, involving various wind speeds and bluff body geometries, demonstrate the robust correlation between the extracted continuous manifold in Fourier spectra and wind speed. By incorporating a feed‐forward network alongside the autoencoder, wind speed information even under strong turbulence is extracted. Moreover, the deep learning method's ability to classify different bluff bodies, independent of wind speed, is investigated. The findings reveal a unique capability to fingerprint turbulence and distinguish them for various applications. This research showcases the potential of our deep learning‐based MEMS systems for enhancing fluid dynamic sensing and classification tasks.

Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement

The use of sensors in monitoring lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics of delivery van for cargo securing, which is essential to the decision of where accelerometer sensors should be placed when monitoring accelerations or performing cargo securing tests. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle and load. Manual labeling of acceleration events has been used to identify different lateral acceleration events and regression analysis to determine the relationship between lateral accelerations at different sensor positions. The level of acceleration on the roof of the vehicle was found to be like that occurring on a lashed load with limited movements. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. The values of the mean lateral accelerations of the delivery van from the sensor on the roof of the vehicle are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

TeO2 doped ZnO nanostructure for the enhanced NO2 gas sensing on MEMS sensor device

Severe health issues occur when human beings are exposed to NO2 gas, which is formed from combustion processes and reactions facilitated by sunlight. In order for safety measurements, NO2 gas sensors are highly needed at low concentration gas detections. ZnO gas sensor with the doping of TeO2 is developed using co-sputtering technique. ZnO/TeO2 heterojunction nanostructure is analyzed by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM) and energy dispersive spectroscopy (EDS) for structural study. Gas sensing properties were tested with Micro electro mechanical system (MEMS) devices, on which ZnO/TeO2 nanostructure is developed. This study focused on doping of 4 different variations of TeO2 with ZnO such as 0%, 2%, 4% and 8% atomic weight percentage (at.w%) of TeO2. Upon testing, 4% ZnO/TeO2 transpired to be best possible parameter for NO2 gas sensing. With maximum NO2 gas concentration of 1 ppm, 4% ZnO/TeO2 sensor exhibited 80% sensing response and with 200 ppb of minimum gas concentration, sensor had 42% response at optimal temperature of 100 oC. TeO2 doped ZnO sensor exhibits superior performance over pure ZnO sensor and has good selectivity over NO2 gas.

Machine learning methods for multi-rotor UAV structural damage detection based on MEMS sensor

Multi-rotor Unmanned Aerial Vehicles (UAVs) have become increasingly important in industries and early detection of structural damage is crucial to prevent unexpected breakdowns, ensure production efficiency, and maintain operational safety. This paper proposes machine learning techniques for detecting damage caused by loosened screws which is not easy founded based on vibration signals. An independent data acquisition device with a Micro Electro Mechanical Systems (MEMS) sensor is designed and fixed onto the multi-rotor UAVs to acquire the vibration data. Four machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, and Random Forest, are employed for damage detection. The results demonstrate successful utilization of the vibration data from the MEMS sensor for damage detection, with the random forest model outperforming other models with an accuracy of 90.07.

Applications of a Novel Tunable Piezoelectric Vibration Energy Harvester.

Conversion of ambient energy to usable electrical energy is attracting attention from researchers since providing a maintenance-free power source for the sensors is critical in any IoT (Internet of Things)-based system and in SHM (structural health monitoring). Continuous health monitoring of structures is advantageous since the damage can be identified at inception and the necessary action taken. Sensor technology has advanced significantly, and MEMS (microelectromechanical systems)-based low-power sensors are available for incorporating into large structures. Relevant signal conditioning and transmission modules have also evolved, making them power-efficient and miniaturized. Various micro wireless sensor nodes (WSN) have also been developed in recent years that require very little power. This paper describes the applications of a novel tunable piezoelectric vibration energy harvester (PVEH) for providing autonomous power to low-power MEMS sensors for use in IoT and remote SHM. The novel device uses piezoelectric material and an ionic polymer-metal composite (IPMC) and enables electrical tuning of the resonant frequency using a small portion of the power generated.

Semantic Proximity Update of GNSS/INS/VINS for Seamless Vehicular Navigation Using Smartphone Sensors

The advancement of microelectromechanical systems (MEMS) and the Internet of Things (IoT) have enabled a wide range of applications based on smartphones. However, the existing navigation methods using these low-cost MEMS sensors cannot provide acceptable information for location-based applications in various environments. Their technical limitations, such as severe signal attenuation, reflections, blockages, error accumulation, and low quality of images degrade the performance of GNSS, INS, and camera. To mitigate these limitations, especially in indoor vehicle navigation, we first analyze the performance of the existing fusion algorithm, then we propose Semantic Proximity Update (SPU) based on a pre-trained model of real-time object detection to enhance the integration of Global Navigation Satellite System (GNSS), Inertial Navigation System (INS) and Visual Inertial Navigation System (VINS). SPU consists of the detection of geo-referenced objects and the relative movement to infer the absolute position. The proposed INS/GNSS/VINS/SPU can maintain long-term acceptable accuracy regardless of the indoor/outdoor environment. It only requires the use of smartphone sensors; thus, this scheme has no additional cost for users. Experimental results indicated that the errors of this scheme in horizontal positioning and three-dimensional positioning were 51.6% and 86.8% lower, respectively than those of a conventional integration.

Investigating capacitive force sensors with 3D printed flexible structures as dielectric layers

The utilization of a three dimension (3D) printing for the fabrication of micro-electromechanical system (MEMS) sensors presents various advantages including low cost, customization, simplicity, and speed. This study involved conducting mechanical and electrical analyses on three types of dielectric layer structures using the finite element method. Capacitive force sensors were produced through 3D printing using fused deposition modeling (FDM). The sensitivity and stiffness of the sensors were determined by measuring their capacitance and displacement changes under force using a self-made measurement system. The experimental results indicate that pre-heating the dielectric layer can reduce the hysteresis effects. In terms of more complex structures, stringing phenomena during the printing process can compromise the reliability of the sensor. A suitable dielectric layer structure can be designed based on the measurement range and sensitivity of the sensor. The results of this study demonstrate the potential for additive manufacturing to replace conventional MEMS processes in sensor production. With the advancement of FDM printing technology, it is increasingly feasible to apply 3D printing technology to sensor manufacturing in the future.

Acoustic Sensor Development for Quench Detection in HTS Conductors

A MEMS (Micro Electro-Mechanical System) acoustic sensor-array quench detection method has been proposed for quick, unobtrusive, and precisely localized detection of quench events in superconducting magnets. A cryogenic probe was designed and built to study the quench event in a gaseous coolant induced in a segment of an HTS tape and verify the effectiveness of acoustic MEMS sensors in detecting such events. Prior work characterized the performance of a commercially available Vesper MEMS microphone, and associated amplifiers, in cryogenic conditions which were utilized in this work. Critically, the application specific integrated circuit (ASIC) used to amplify the MEMS piezo-sensor output signal fails to operate at temperatures below 110 K. Thus, the ASIC was removed from the microphone package and replaced with a custom low temperature preamplifier which we have shown performs with no loss of function at temperatures as low as 10 K. The quench experiment is conducted using the probe within a two-stage Gifford-McMahon (GM) cryocooler in a gaseous helium environment, at cryogenic temperatures between 20 K to 60 K. Three temperature sensors within the sample region monitor temperature of the HTS sample tape and environment. The HTS sample, a 2 mm wide REBCO tape, is mounted within a square G10 tube. A linear array of the repackaged sensors is mounted to the tube to detect the quench event. The quench detection behavior using the sensors is compared with the readings from voltage taps on the sample. The response characteristics of the Vesper acoustic piezo-sensor with the newly identified, cryogenic-safe preamplifier in detecting quench events of REBCO tapes in gaseous helium are presented.

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

Groundwater level monitoring is critical to the protection and management of groundwater resources. Properly designed and executed instrumentation can play an important role in increasing the quality and reliability of collected data and reducing total monitoring costs. The efficiency of the instrumentation depends mainly on the accuracy and reliability of the installed sensors. This study presents the testing and application of a cost-effective pressure sensor (0–689 kPa range) for water level monitoring based on microelectromechanical system (MEMS) technology and the internet of things concept. The sensor performance, in terms of accuracy, precision, repeatability, and temperature, was investigated in laboratory columns (with constant water level, increasing and decreasing water levels at various rates) and in situ conditions in an observation bore (with natural groundwater level fluctuations). The results show that the MEMS sensor is capable of providing a reliable and adequate monitoring scheme with an accuracy of 0.31% full scale (FS) (2.13 kPa).