Microelectromechanical Systems Accelerometer Research Articles

Design and Experimental Assessment of Low-Noise Piezoelectric Microelectromechanical Systems Vibration Sensors.

The ubiquity of vibration sensors and accelerometers, as well as advances in microfabrication technologies, have led to the development of implantable devices for biomedical applications. This work describes a piezoelectric microelectromechanical systems accelerometer designed for potential use in auditory prostheses. The design includes an aluminum nitride bimorph beam with a silicon proof mass. Analytic models of the device sensitivity and noise are presented. These lead to a minimum detectable acceleration cost function for the sensor that can be used to optimize sensor designs more effectively than typical sensitivity maximizing or electrical noise minimizing approaches. A fabricated device with a 1 μm thick, 100 μm long, and 700 μm wide beam and a 400 μm thick, 63 μm long, and 740 μm wide proof mass is tested experimentally. Results indicate accurate modeling of the system sensitivity up to the first resonant frequency (1420 Hz). The low-frequency sensitivity of the device is 1.3 mV/g, and the input referred noise is 36.3 at 100 Hz and 11.8 at 1 kHz. The resulting minimum detectable acceleration at 100 Hz and 1 kHz is 28 and 9.1 , respectively. A brief explanation of the use of the validated cost function for sensor design is provided, as well as an example comparing the piezoelectric sensor design to another from the literature. It is concluded that a traditional single-resonance design cannot compete with the performance of acoustic sensors; therefore, novel device designs must be considered for implantable auditory prosthesis applications.

Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy.

The paper deals with performance enhancement of low-cost, consumer-grade inertial sensors realized by means of Micro Electro-Mechanical Systems (MEMS) technology. Focusing their attention on the reduction of bias instability and random walk-driven drift of cost-effective MEMS accelerometers and gyroscopes, the authors hereinafter propose a suitable method, based on a redundant configuration and complemented with a proper measurement procedure, to improve the performance of low-cost, consumer-grade MEMS sensors. The performance of the method is assessed by means of an adequate prototype and compared with that assured by a commercial, expensive, tactical-grade MEMS inertial measurement unit, taken as reference. Obtained results highlight the promising reliability and efficacy of the method in estimating position, velocity, and attitude of vehicles; in particular, bias instability and random walk reduction greater than 25% is, in fact, experienced. Moreover, differences as low as 0.025 rad and 0.89 m are obtained when comparing position and attitude estimates provided by the prototype and those granted by the tactical-grade MEMS IMU.

Silicon MEMS inertial sensors evolution over a quarter century

Silicon-based microelectromechanical systems (MEMS) inertial sensors have become ubiquitous, revolutionizing motion sensing, vibration sensing and accurate positioning in several societal fields. Driven by consumer and automotive markets, companies involved in this technological development focused mostly on low cost, miniaturization and low power consumption, somewhat sacrificing measurement accuracy. In several laboratories all over the world, however, the research toward higher-performance sensors has been going on for more than two decades, with the goal of improving two key parameters for future applications: noise density and bias stability. This review article summarizes, for silicon-based MEMS accelerometers and gyroscopes, the most relevant working principles that appeared in the scientific literature. The collection of several data about the above mentioned key figures enables tracing the roadmap for further developments in the upcoming decade.

Simultaneous compensation of systematic errors of a low-cost MEMS triaxial accelerometer and its temperature dependency without accurate laboratory equipment

PurposeLaboratory calibration methods are time-consuming and require accurate devices to find the error coefficients of the low-cost microelectromechanical system (MEMS) accelerometer. Besides, low-cost MEMS sensors highly depend on temperature because of their silicon property and the effect of temperature on error coefficients should also be considered for compensation. This paper aims to present a field calibration method in which the accelerometer is placed in different positions without any accurate equipment in a few minutes and its temperature is changed by a simple device like a hairdryer.Design/methodology/approachIn this paper, a non-linear cost function is defined based on this rule that the magnitude of the acceleration measured by the accelerometer in static mode is equal to the gravity plus error factors. Also, the dependency of error coefficients of the accelerometer is presented as a second-order polynomial in this cost function. By minimizing the cost function, the accelerometer error coefficients include bias, scale factor and non-orthogonality and their temperature dependency are obtained simultaneously.FindingsSimulation results in MATLAB and empirical results of a MPU6050 accelerometer verify the good performance of the proposed calibration method.Originality/valueFinding a fast and simple field calibration method to calibrate a low-cost MEMS accelerometer and compensate for the temperature dependency without using accurate laboratory equipment can help a wide range of industries that use advanced and expensive sensors or use expensive laboratory equipment to calibrate their sensors, to decrease their costs.

Small Local Earthquake Detection Using Low-Cost MEMS Accelerometers: Examples in Northern and Central Italy

Abstract This study evaluates the seismicity detection efficiency of a new low-cost triaxial accelerometer prototype based on microelectromechanical systems (MEMS) technology. Networks of MEMS sensors were installed in telecommunication infrastructures to build two small arrays in northern and central Italy. The sensor prototypes recorded major earthquakes as well as nine small seismic events with 2.0<ML<3.0. Where possible, MEMS were compared to the closest high-quality seismic stations belonging to the national accelerometric network. The comparison, in terms of peak ground accelerations and spectral responses, confirms that the signals are in good agreement. The tested inexpensive MEMS sensors were able to detect small local events with epicentral distances as large as 50 km and provided an efficient characterization of the main motion parameters. This confirms that the proposed accelerometer prototypes are promising tools to integrate into traditional networks for local seismicity monitoring.

Body Temperature-Indoor Condition Monitor and Activity Recognition by MEMS Accelerometer Based on IoT-Alert System for People in Quarantine Due to COVID-19.

Coronavirus disease 19 (COVID-19) is a virus that spreads through contact with the respiratory droplets of infected persons, so quarantine is mandatory to break the infection chain. This paper proposes a wearable device with the Internet of Things (IoT) integration for real-time monitoring of body temperature the indoor condition via an alert system to the person in quarantine. The alert is transferred when the body thermal exceeds the allowed threshold temperature. Moreover, an algorithm Repetition Spikes Counter (RSC) based on an accelerometer is employed in the role of human activity recognition to realize whether the quarantined person is doing physical exercise or not, for auto-adjustment of threshold temperature. The real-time warning and stored data analysis support the family members/doctors in following and updating the quarantined people’s body temperature behavior in the tele-distance. The experiment includes an M5stickC wearable device, a Microelectromechanical system (MEMS) accelerometer, an infrared thermometer, and a digital temperature sensor equipped with the user’s wrist. The indoor temperature and humidity are measured to restrict the virus spread and supervise the room condition of the person in quarantine. The information is transferred to the cloud via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker. The Bluetooth is integrated as an option for the data transfer from the self-isolated person to the electronic device of a family member in the case of Wi-Fi failed connection. The tested result was obtained from a student in quarantine for 14 days. The designed system successfully monitored the body temperature, exercise activity, and indoor condition of the quarantined person that handy during the Covid-19 pandemic.

A Low-g MEMS Accelerometer with High Sensitivity, Low Nonlinearity and Large Dynamic Range Based on Mode-Localization of 3-DoF Weakly Coupled Resonators.

This paper presents a new design of microelectromechanical systems (MEMS) based low-g accelerometer utilizing mode-localization effect in the three degree-of-freedom (3-DoF) weakly coupled MEMS resonators. Two sets of the 3-DoF mechanically coupled resonators are used on either side of the single proof mass and difference in the amplitude ratio of two resonator sets is considered as an output metric for the input acceleration measurement. The proof mass is electrostatically coupled to the perturbation resonators and for the sensitivity and input dynamic range tuning of MEMS accelerometer, electrostatic electrodes are used with each resonator in two sets of 3-DoF coupled resonators. The MEMS accelerometer is designed considering the foundry process constraints of silicon-on-insulator multi-user MEMS processes (SOIMUMPs). The performance of the MEMS accelerometer is analyzed through finite-element-method (FEM) based simulations. The sensitivity of the MEMS accelerometer in terms of amplitude ratio difference is obtained as 10.61/g for an input acceleration range of ±2 g with thermomechanical noise based resolution of 0.22 and nonlinearity less than 0.5%.

Optimized Railway Track Condition Monitoring and Derailment Prevention System Supported by Cloud Technology

This paper describes an easy way to monitor railway track abnormalities and update information on the track’s status to the cloud. Abnormalities present in railway tracks should be identified promptly and rectified to ensure safe and smooth travel. In this paper, a cloud-based track monitoring system (CTMS) is proposed for the monitoring of track conditions. The micro-electro mechanical systems (MEMS) accelerometers which are mounted in the axle are used to measure the railway track abnormality. The measured signal is optimized using the flower pollination optimization algorithm (FPOA). Because of signaling problems in the global positioning system (GPS), it is difficult to estimate the exact location of the abnormality in real time. A new method is introduced to overcome this problem. It provides the location of an abnormality even when the GPS signal is absent. The performance of the CTMS is compared with three different speed scenarios of the vehicle. The information about the abnormality on the track can be shared with other trains that pass through the same location so that the driver can reduce speed in that location to avoid derailment. Finally, an experimental setup was developed and the performance of CTMS is studied under four different irregularity cases.

Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.

Application of the MEMS Accelerometer as the Position Sensor in Linear Electrohydraulic Drive.

Various distance sensors are used as measuring elements for positioning linear electrohydraulic drives. The most common are magnetostrictive transducers or linear variable differential transformer (LVDT) sensors mounted inside the cylinder. The displacement of the actuator’s piston rod is proportional to the change in the value of the current or voltage at the output from the sensor. They are characterized by relatively low measurement noise. The disadvantage of presented sensors is the need to mount them inside the cylinders and the high price. The article presents preliminary research on the replacement of following sensors and the use of a microelectromechanical system (MEMS) accelerometer as a measuring element in the electrohydraulic drive control system. The control consisted of two phases: at first, the signal from the acceleration sensor was analyzed during the actuator movement, based on the value determined from the simplified model implemented on the controller. In the range of motion in which the dynamics were the lowest, the signal was integrated and the obtained value was used in the second phase of motion. In the correction phase, a new set point was determined. Conducting the research required building a dedicated research stand. The author conducted the simulation and experimental research.

Micro-Opto-Electro-Mechanical System Accelerometer Based on Coarse-Fine Processing of Fabry–Perot Interferometer Signals

The use of micro-electro-mechanical system (MEMS) accelerometers in aircraft control and navigation systems is of great interest. In this paper, a MEMS accelerometer with optical reader based on a two-channel Fabry–Perot interferometer is considered. To improve the accelerometer accuracy, it is proposed to analyze the fractional part of the interference band. The authors have studied the linear acceleration effect on the variation of output signal of the optical reader at various beam sensor parameters, as well as the variation of interferometer transmittance at different mirror reflectivity. Based on the interferometer transmittance vs. displacement relationships, the channel switching conditions were established to enable bypassing low-sensitivity zones. A method of optical signal processing has been developed, which generates the output value by concatenating the coarse measurement results and calculating a fine component. The proposed processing method makes it possible to increase the accuracy of the acceleration measurement results without changing the conversion range.

A Novel Fabrication Method for a Capacitive MEMS Accelerometer Based on Glass-Silicon Composite Wafers.

In this paper, we report a novel teeter-totter type accelerometer based on glass-silicon composite wafers. Unlike the ordinary micro-electro-mechanical systems (MEMS) accelerometers, the entire structure of the accelerometer, includes the mass, the springs, and the composite wafer. The composite wafer is expected to serve as the electrical feedthrough and the fixed capacitance plate at the same time, to simplify the fabrication process, and to save on chip area. It is manufactured by filling melted borosilicate glass into an etched silicon wafer and polishing the wafer flat. A sensitivity of 51.622 mV/g in the range of ±5 g (g = 9.8 ), a zero-bias stability under 0.2 mg, and the noise floor with 11.28 µg/√Hz were obtained, which meet the needs of most acceleration detecting applications. The micromachining solution is beneficial for vertical interconnection and miniaturization of MEMS devices.

Estimation of Displacement Response in Steel Plate Girder Bridge Using a Single MEMS Accelerometer

Live load-induced deformation is a significant evaluation index for bridge maintenance. To measure a structural displacement response, a method using several MEMS (Micro Electro Mechanical Systems) accelerometers has previously been developed. However, this method requires several sensors, and for improved practicality, a reduction in the number of sensors is desired. Therefore, in this article, a simple displacement monitoring method is proposed that uses only one MEMS accelerometer, which is installed at the longitudinal center of the target bridge. A wavelet transform-based method is applied to the acceleration records to identify the integration range for displacement estimation. The accuracy was evaluated on an actual in-service bridge. The result indicates high accuracy of displacement measurements, with only 5% error when compared with the values recorded by a contact displacement gauge.

On the feasibility of IoT-based smart meters for earthquake early warning

The microelectromechanical systems (MEMS) accelerometer built into a smart meter (SM) has a nominal digital resolution of 16 bits. However, this resolution collapses to 7 bits of information per sample when used in an urban environment. This collapse in resolution limits the sensitivity required to effectively operate the earthquake early warning platform (EEWP). In this study, we evaluate the performance of the MEMS sensor in present SMs with respect to a reference sensor, with a special focus on its poor noise power spectral density (PSD, [Formula: see text]). We also explore the general capacity of the SM in an IoT-based EEWP and provide explicit information regarding the 16-bit digital MEMS accelerometer. Then, we investigate the functionality of the sensor in the context of event detection in the presence of background vibration. When the value of acceleration root mean square (RMS) exceeds 20 mg, the meter’s error decreases to <20%, whereas the peak ground acceleration error decreases to <20% for the peak value greater than ~70 mg. The MEMS sensor is unreliable for motions with a peak acceleration of less than 148 mg or those with an RMS value less than 46 mg. However, we note that SMs exhibit reasonable amplitude and phase coherence for frequencies above 1 Hz with respect to the reference accelerometer. To enhance the sensitivity, averaging 1000 coherent accelerometer observations enhances the digital resolution to 14 bits, which allows the efficient usage of the network bandwidth. Since the accelerometer is used as an anti-tampering mechanism, the SM is similar to a tiltmeter. Therefore, it is necessary to reconfigure SMs for early warning systems. Despite the challenges, the use of SM for an IoT-based EEWP is technically feasible.

Improving Accuracy of Low-Cost Vehicle Attitude Estimation by Denoising MIMU Based on SSA and ICA

In this article, we develop a novel method to improve the accuracy of the low-cost vehicle attitude estimation using singular spectrum analysis (SSA) and independent component analysis (ICA). The microelectromechanical system (MEMS) accelerometers and the MEMS gyros are fused by the Kalman filtering algorithm to provide a low-cost attitude estimation. The low-frequency vibration noise and the maneuvering acceleration disturbance in the measurements of the MEMS accelerometers introduce large errors to the attitude estimation. To mitigate the low-frequency vibration noise, we propose a method based on SSA to separate the quasi-periodic components in accelerometer measurements and keep the trend remained as the true attitude measurement. SSA is effective in decomposing original signal into trend, periodic or quasi-periodic components, and thus, the low-frequency vibration noise that behaves as a quasi-periodic signal can be well separated. However, the acceleration disturbance still exists in the trend. Thus, a method based on ICA is then proposed to remove the acceleration disturbance by the aid of the turning rate measurement. The proposed method is verified with driving tests under city road and off-road conditions, showing a significant improvement of the attitude accuracy.

Use of MEMS sensors for condition monitoring of devices: discussion about the accuracy of features for diagnosis

This paper analyses the effect of the variability of metrological characteristics of a set of low-cost Micro Electro-Mechanical Systems (MEMS) for the acceleration measurement, on the calculation of typical features used for condition monitoring (CM) of automatic production lines. The knowledge of the contribution of the variability of metrological characteristics to the final accuracy of features is an aspect of interest when networks of low-cost sensors are used, in particular in case the variability of their characteristics is high. In fact, due to a mass production, the calibration is not carried out sensor by sensor, but the characteristics are determined on a sample basis and assigned to the entire batch. Neglecting the variability between sensors can lead to effects on the results of data analysis, which are not easily predictable. In this paper, the real variability of the sensor's characteristics, experimentally evaluated through the calibration of a set of 25 low-cost MEMS accelerometers, has been taken into account. Digital sensitivity, signal-to-noise ratio and data rate variability of each device have been considered for the analysis. The analysis has been carried out with reference to two different test cases of industrial interest, by modifying the real outputs of high performance piezoelectric accelerometers used for CM, in order to simulate the effect of the metrological characteristics of MEMS sensors. The results show which features, among those typically used for CM, are more affected and which characteristics of MEMS are more influencing the features themselves, with reference to the specific considered applications.

A New Technique on Vibration Optimization of Industrial Inclinometer for MEMS Accelerometer Without Sensor Fusion

Accelerometer of the Microelectromechanical systems (MEMS) based inertial measurement units (IMUs) is key to inclination measurement in the industry 4.0. However, external vibration negatively impacts the precision of orientation angles during operation. Many inclinometer companies have demanded to develop a solution for vibration impact on accelerometer without other sensors' support because of economic problems. This article presents a new algorithm Orientation Axes Crossover Processing (OACP) on vibration optimization for MEMS accelerometer without sensor fusion. The proposed filter works on a principle based on the characteristics of vibration impact on whether the X-axis or Y-axis to optimally minimize the noise. A high accurate setup is built-up based on the Pan-Tilt Unit and a TUMAC vibrator for the verification of new filters, implemented into LSM9DS1 (3D accelerometer, 3D gyroscope). The new filter is able to work independently, and also fuse with the Low-pass filter or Kalman filter to enhance the dynamic response, only 0.163 seconds as maximum delay during vibration. The experimental results show that the proposed algorithm always accomplishes smaller variations than Low-pass filter, about 0.2 degrees in standard deviation. The compromise between vibration immunity and dynamic response is analyzed in detail to demonstrate the optimal performances of the concerned filters. The project was carried out at the `Sensor System' in Italy which is an industrial company in the inclinometer field.

Micro-electromechanical systems-based technologies for leak detection and localization in water supply networks: A bibliometric and systematic review

Leakages from water pipelines cause economic losses and environmental hazards. Despite the damages, it is challenging to avoid leaks throughout the lifetime. However, leak detection and localization, especially in real-time, minimize the damage. Owing to the recent advances, the micro-electromechanical systems (MEMS) based technologies have started to gaining recognition for water network monitoring in real-time, however, a systematic literature review to analyze the existing research trends, technological advances, and future research opportunities are largely missing. This study has based its investigation on three main MEMS-based technologies for real-time monitoring: MEMS sensors wireless networks, MEMS accelerometers, and MEMS hydrophones. Firstly, a scientometric analysis is conducted to 1) retrieve relevant research articles through Scopus, Web of Science, and Google Scholar, 2) visualize the publication trends, and 3) analyze the science mapping of influential authors, countries, organization, and top keywords occurrences. Secondly, qualitative discussions are made on research themes and sub-themes within three technologies: 1) MEMS WSNs are classified into static and mobile sensor-based wireless sensor networks. Seven sub-themes are categorized under static sensor-based wireless sensor networks such as PIPETECT, whereas three sub-themes are categorized under mobile sensor-based WSNs such as TriopusNet; 2) MEMS accelerometers are categorized into accelerometers based machine learning models and wireless systems; and 3) MEMS hydrophones are represented under one category. Thirdly, nine research opportunities including automated models, on-field real network-based experimental studies, optimal placement of sensor nodes for energy savings in wireless sensor networks, and a comparative analysis of real-time technologies are revealed. This study enhances the familiarity of early researchers with the application of MEMS-based technologies for leak detection and localization and provides seasoned researchers with a platform for future research development. • MEMS-based technologies create opportunities to enhance real-time leak detection and localization in water supply networks. • MEMS-based wireless sensor networks, accelerometers, and hydrophones are classified into different themes and sub-themes. • The bibliometric search process, scientometric analysis, and qualitative analyses are discussed in detail. • Future research directions are provided.

Research on High-Resolution Miniaturized MEMS Accelerometer Interface ASIC.

High-precision microelectromechanical system (MEMS) accelerometers have wide application in the military and civil fields. The closed-loop microaccelerometer interface circuit with switched capacitor topology has a high signal-to-noise ratio, wide bandwidth, good linearity, and easy implementation in complementary metal oxide semiconductor (CMOS) process. Aiming at the urgent need for high-precision MEMS accelerometers in geophones, we carried out relevant research on high-performance closed-loop application specific integrated circuit (ASIC) chips. According to the characteristics of the performance parameters and output signal of MEMS accelerometers used in geophones, a high-precision closed-loop interface ASIC chip based on electrostatic time-multiplexing feedback technology and proportion integration differentiation (PID) feedback control technology was designed and implemented. The interface circuit consisted of a low-noise charge-sensitive amplifier (CSA), a sampling and holding circuit, and a PID feedback circuit. We analyzed and optimized the noise characteristics of the interface circuit and used a capacitance compensation array method to eliminate misalignment of the sensitive element. The correlated double sampling (CDS) technology was used to eliminate low-frequency noise and offset of the interface circuit. The layout design and engineering batch chip were fabricated by a standard 0.35 μm CMOS process. The active area of the chip was 3.2 mm × 3 mm. We tested the performance of the accelerometer system with the following conditions: power dissipation of 7.7 mW with a 5 V power supply and noise density less than 0.5 μg/Hz1/2. The accelerometers had a sensitivity of 1.2 V/g and an input range of ±1.2 g. The nonlinearity was 0.15%, and the bias instability was about 50 μg.

Three-position characterization for the adjustment of MEMS accelerometer scale factor

Up/down static and the six-position least squares calibration methods are commonly used in research conducted on adjustment of the micro-electromechanical systems (MEMS) accelerometer scale factor (or sensitivity). When applying these methods, it is difficult to adjust the accelerometer measuring axis parallel to the gravity direction, causing an error in the accelerometer scale factor. In this paper, a three-position scale factor characterization that does not require corrections of the precise coincident of the measuring axis with the direction of gravity is studied. The method uses the up/down calibration method to obtain the coarse scale factor and bias. The coarse adjustment coefficient is then used to convert the accelerometer output data in the horizontal, up, and down positions into acceleration. The horizontal acceleration is used to estimate the scale factor error. Next, the estimated scale factor error is used to modify the coarse scale factor, producing a more accurate scale factor. The adjustment results from simulation and experiments on triaxial MEMS accelerometers show that the proposed method can effectively correct the scale factor error caused by inability to align the accelerometer measurement axis parallel to the gravity line, with a relative scale factor error of 0.07%, improving the accuracy of the accelerometer scale factor.