Microelectromechanical Systems Sensors Research Articles

MEMS-based electronic nose system for measurement of industrial gases with drift correction methodology

This work explores the use of microelectromechanical system (MEMS) sensors in electronic nose systems for the measurement of prominent industrial pollutants and proposes a methodology for the correction of drift induced in sensors during prolonged use for measurement of corrosive compounds. MEMS sensors have the advantages of low recovery and response time, lower power consumption and improved sensitivity. An electronic nose system is developed to quantify the four major pollutants emitted from various process industries, i.e. hydrogen sulfide (H2S), carbon monoxide (CO), ammonia (NH3) and ethanol (C2H5OH). All four pollutants represent major environmental and human health hazards and rapid quantification is vital. A drift correction algorithm was developed using experimental design and robust regression to correct the drift induced in the sensors. The experiments consisted of eight experimental sets with mixtures of H2S, CO, NH3 and C2H5OH measured on the same sensor array at the start and after a gap of 1 year. The methodology consisted of mapping the response of sensors after 1 year compared with that observed at the very start (day 1) of the experiment. The results showed successful implementation of the methodology, with the root mean square error values being significantly reduced at 0.235, 0.354, 0.145 and 0.651, respectively, for the four studied odorants (H2S, NH3, CO, and C2H5OH).

Triple sensing scheme based on nonlinear coupled micromachined resonators

In the past few decades, advances in micro-electromechanical systems (MEMS) have produced robust, accurate, and high-performance devices. Extensive research has been conducted to improve the selectivity and sensitivity of MEMS sensors by adjusting the device dimensions and adopting nonlinear features. However, sensing multiple parameters is still a challenging topic. Except for the limited research focus on multi-gas and multimode sensing, detecting multiple parameters typically relies on combining several separate MEMS sensors. In this work, a new triple sensing scheme via nonlinear weakly coupled resonators is introduced, which could simultaneously detect three different physical stimuli (including longitudinal acceleration) by monitoring the dynamic response around the first three lowest vibration modes. The Euler–Bernoulli beam model with three-mode Galerkin discretization is used to derive a reduced-order model considering the geometric and electrostatic nonlinearities to characterize the resonator's nonlinear dynamics under the influence of different stimuli. The simulation results show the potential of the nonlinear coupled resonator to simultaneously perform triple detection.

Understanding the role of particle rotation in asphalt mixture compaction by tracking coarse aggregate movement

Compaction is one of the most critical steps in asphalt pavement construction. Understanding the compaction mechanism is essential to improving field and laboratory compaction. Recent studies have transformed from macro- to meso- scale to characterize the compaction process from the perspective of the internal mixture. It has demonstrated that rotation is a critical motion for particles in a confined particulate structure, whereas how particle rotation influences the compaction process in a particulate media is still unclear. This study employs a particle size microelectromechanical systems (MEMS) sensor, SmartRock, and discrete element modeling to track particle movement of asphalt mixture in Superpave gyratory compaction (SGC) for three typical gradation types (dense-graded AC, gap-graded SMA, and open-graded PFC). Results confirm that the particle’s relative rotation could characterize SGC compaction as a two-stage process divided by transition cycle N. Such stage characteristics could be fundamentally related to the mixture’s packing features at the particle scale. The N value decreases in the order of PFC, SMA, and AC with the increase in the Coarse Aggregate (CA) ratio, which indicates that mixtures with a stronger aggregate structure (such as PFC) will be harder to stabilize under compaction loading. The particle relative rotation transition cycle N could be used as a particle-scale index to characterize the internal structure during compaction. Further, in SGC discrete element model, particle rotation was found to be critical in affecting the particle’s horizontal translation and, ultimately material’s densification, which further explains the compaction mechanism of particulate materials.

Enhanced toluene gas-sensing properties of MEMS sensor based on Pt-loaded SnO2 nanoparticles

Rapid detection of low concentration toluene is highly desirable in environment monitoring, industrial processes, medical diagnosis, etc. In this study, we prepared Pt-loaded SnO2 monodispersed nanoparticles through hydrothermal method and assembled a sensor based on micro-electro-mechanical system (MEMS) to detect toluene. Compared with the pure SnO2, the 2.92 wt% Pt-loaded SnO2 sensor exhibits a 2.75 times higher gas sensitivity to toluene at about 330 °C. Meanwhile, the 2.92 wt% Pt-loaded SnO2 sensor also has a stable and good response to 100 ppb of toluene. Its theoretical detection limit is calculated as low as 12.6 ppb. Also, the sensor has a short response time of ∼10 s to different gas concentrations, as well as the excellent dynamic response—recovery characteristics, selectivity, and stability. The improved performance of Pt-loaded SnO2 sensor can be explained by the increase of oxygen vacancies and chemisorbed oxygen species. The electronic and chemical sensitization of Pt to SnO2-based sensor, together with small size and fast gas diffusion of the MEMS design ensured fast response and ultra-low toluene detection. This provides new ideas and decent prospect for developing miniaturized, low-power-consumption, and portable application of gas sensing devices.

MEMS sensor based on MOF-derived WO3-C/In2O3 heterostructures for hydrogen detection

Rational structure design of sensing materials is the most effective way to obtain a hydrogen (H2) sensor with optimal performance. Herein, a porous heterostructure WO3-C/In2O3 was designed and prepared by in-situ coupling carbon layer and WO3 into MOF-derived (metal organic framework) In2O3. In combination with Micro-Electro-Mechanical System (MEMS), a miniature H2 sensor using WO3-C/In2O3 as the active sensing material was fabricated. Compared with the bare In2O3 sensor, the sensor based on WCI-9 (the mass ratio of WO3 to In2O3 in WO3-C/In2O3 is 9 wt%) shows higher response value and lower operating temperature (Ra/Rg = 10.11@ 1000 ppm; 250 °C). Moreover, the WCI-9 sensor possesses a fast response-recovery speed (1.9/9.2 s@200 ppm) and a low limit of detection (LOD) (5 ppm) for H2. The good performance of the WCI-9 sensor is attributed to the hierarchical porous structure and multicomponent heterojunctions present in the material. This work not only provides an effective method for H2 detection, but also a strategy for constructing sensing materials with multicomponent heterojunctions.

Analysis of MEMS sensor application and prospects

The development trends in modern science and technology are miniaturization, integration, and intelligence. Microsensors have developed rapidly with the development of micro-electromechanical systems (MEMS) and micromachining technology. In this paper, the concepts and types of MEMS sensors are summarized by literature and case analyses, and their research statuses and application fields are briefly introduced. The development trends of MEMS sensors are discussed. This paper finds that combining sensors with intelligent products can achieve another application climax of MEMS sensors.

What MEMS Research and Development Can Learn from a Production Environment

The intricate interdependency of device design and fabrication process complicates the development of microelectromechanical systems (MEMS). Commercial pressure has motivated industry to implement various tools and methods to overcome challenges and facilitate volume production. By now, these are only hesitantly being picked up and implemented in academic research. In this perspective, the applicability of these methods to research-focused MEMS development is investigated. It is found that even in the dynamics of a research endeavor, it is beneficial to adapt and apply tools and methods deduced from volume production. The key step is to change the perspective from fabricating devices to developing, maintaining and advancing the fabrication process. Tools and methods are introduced and discussed, using the development of magnetoelectric MEMS sensors within a collaborative research project as an illustrative example. This perspective provides both guidance to newcomers as well as inspiration to the well-versed experts.

Serial and Parallel Connections of Micromechanical Resonators for Sensing: Theories and Applications

The mass-spring-damper resonator is the basic mechanical component of current microelectromechanical systems (MEMS) sensors. It is typically implemented in various forms of micromechanical cantilevers, beams, diaphragms, rings, etc. In the classical sensing paradigm, the amplitude, frequency, or phase change of a single degree-of-freedom (1-DoF) resonator is used to perform sensing applications. This paper presents the basic theories and applications for serial and parallel connections of 1-DoF micromechanical resonators for sensing. In the serial connection, the amplitude ratio of the two outer resonators is typically selected as the output metric and the mode-localization phenomenon is especially used to mechanically amplify the signal and suppress common-mode noises. Then the signal-to-noise ratio (SNR) of sensors can be improved. In the parallel connection, resonators are combined by an optimal filter to suppress random noises and the SNR can be further improved. Unknown disturbances can also be suppressed by differential sensing in the parallel connection. In a short summary, the serial and parallel connection provide calculation of summation, subtraction, and division, thus enabling further sensing performance improvement. And full compatibility to current MEMS processes provides a new sensing paradigm to improve the performance of MEMS sensors. 2021-0158

Direction of Arrival Algorithm for Acoustic Sensors Operating Near Resonance

Recently, it has been possible to detect sound direction using phase-sensitive sensors placed much closer to each other than the sound wavelength. A typical setup combines two phase-sensitive sensors placed perpendicular to each other with a third omnidirectional sensor, the latter to determine the bearing quadrant. So far, algorithms that extract the sound direction from the sensor voltages have only been applicable to sensors with identical frequency responses. However, we have developed microelectromechanical systems (MEMS) sensors with simple harmonic oscillator-like resonance behavior for which these algorithms no longer apply. The operation near resonance greatly increases their performance, but the frequency dependencies of any two sensors are different from each other and from the omnidirectional sensor. In this letter, we show how to accommodate these differences, first, by forcing the two sensor peaks to conform to each other in both magnitude and phase, and second, by adjusting the phase of the omnidirectional sensor to approximately match the phases of the resonant sensors near their resonant frequencies. We then present two algorithms to extract the sound direction. We lay out the theoretical basis for our algorithms and present measurements of acoustic noise to show the algorithms’ effectiveness.

Research advancements in ocean environmental monitoring systems using wireless sensor networks: a review

The ocean environment monitoring system is of great significance to the researchers because the ocean is the storehouse of natural resources. It is critical to comprehend and assess the ocean’s environmental conditions. Several studies have been conducted over the last several decades that use sophisticated information and communication techniques to ensure the ocean ecosystem. Wireless sensor networks (WSNs) are a promising technology to monitor the ocean environment, which delivers significant benefits such as enhanced accuracy and real-time observations. The advancements in sensor technology such as micro electromechanical systems (MEMS), integrated systems, distributed processing, wireless communications, and wireless sensor applications have contributed to the development of WSNs. This paper describes the utilization of WSN and analyzes the previous and existing project works and technologies used for ocean environment monitoring through WSNs, and also includes the MEMS sensor technology used for monitoring various ocean parameters such as ocean wave monitoring, water conductivity, temperature, and depth of ocean.

Wearable Multi-Channel Pulse Signal Acquisition System Based on Flexible MEMS Sensor Arrays with TSV Structure.

Micro-electro-mechanical system (MEMS) pressure sensors play a significant role in pulse wave acquisition. However, existing MEMS pulse pressure sensors bound with a flexible substrate by gold wire are vulnerable to crush fractures, leading to sensor failure. Additionally, establishing an effective mapping between the array sensor signal and pulse width remains a challenge. To solve the above problems, we propose a 24-channel pulse signal acquisition system based on a novel MEMS pressure sensor with a through-silicon-via (TSV) structure, which connects directly to a flexible substrate without gold wire bonding. Firstly, based on the MEMS sensor, we designed a 24-channel pressure sensor flexible array to collect the pulse waves and static pressure. Secondly, we developed a customized pulse preprocessing chip to process the signals. Finally, we built an algorithm to reconstruct the three-dimensional pulse wave from the array signal and calculate the pulse width. The experiments verify the high sensitivity and effectiveness of the sensor array. In particular, the measurement results of pulse width are highly positively correlated with those obtained via infrared images. The small-size sensor and custom-designed acquisition chip meet the needs of wearability and portability, meaning that it has significant research value and commercial prospects.

Autonomous Instrumental Error Correction for a Strapdown Inertial Navigation System

Microelectromechanical systems (MEMS)-based strapdown navigation systems offer advantages such as small size, low cost, and minimal power consumption. However, MEMS sensors are prone to significant low-frequency noise and poor bias repeatability, which can lead to navigational errors over time. These errors make them unsuitable for autonomous navigation applications, even with frequent recalibration. One way to solve this problem is the rotation modulation method. This approach is widely recognized but has only been successful with precise laser and fiber optic gyroscopes equipped with precise rotating platforms. This article focuses on the potential of adapting the rotation modulation method for the case of inexpensive MEMS sensors that can significantly improve navigation performance while maintaining the benefits of microelectromechanical technologies. Potential issues of implementation were discussed, and corresponding requirements were formulated. The proposed optimal computation scheme was verified during static tests of the developed inertial measurement unit (IMU). The rotation of the IMU is capable of harmonically modulating the quasi-static errors of inertial sensors, which are practically eliminated during the navigational algorithm processing estimation, which paves the way for a significant reduction of its navigation errors and increases its autonomous operation time. The static tests conducted in laboratory conditions confirmed the research method's high technical potential. It has been shown that the proposed method is effective for various spatial orientations of the rotational modulation axis. Installing an IMU on a non-steering car wheel allows for abandoning an artificial rotational motion source while preserving the proposed method's advantages. The proposed implementation's main limitation is that the IMU's output data update frequency becomes a multiple of the frequency of the modulating rotation. Accordingly, this frequency should be at least twice the maximum frequency of body manoeuvres to ensure their observability. Additionally, a critical issue is the compensation of the angular velocity of modulation from gyroscope readings. However, despite the mentioned considerations, such an implementation of IMU modulation by rotation still has enormous potential for application in terrestrial navigation.

Prediction of displacement in Reinforced concrete based on artificial neural networks using sensors

Although structural health monitoring (SHM) is widely used in civil engineering, researchers are working to enhance its accuracy. In previous laboratory experiments, four types of sensors, namely force resisting sensor (FSR), piezoelectric sensor (PZS), microelectromechanical system (MEMS) accelerometer sensor, and flex sensor (FLS), were employed to predict load-displacement in Reinforced Concrete (RC) beams subject to monotonic two-point loading. Static loading was performed in the laboratory. The results demonstrated that MEMS sensors outperformed the other three sensors. Consequently, only MEMS sensors were used in the ANN model to predict the error percentage. This study compared the static results of previous experiments to those obtained from an Artificial Neural Network (ANN) model that used the beam's position and load as input parameters to predict displacement. The results revealed that the non-linear ANN model produced positive outcomes. Therefore, SHM is likely to utilize MEMS sensors in various civil engineering applications.

Improvement and compensation of temperature drift of scale factor of a SOI-based MEMS differential capacitive accelerometer

In recent years, the analysis and improvement of temperature characteristics of Si-based capacitive accelerometers has received considerable research attention in the field of Microelectromechanical system (MEMS) sensors. Generally, the influence of temperature on the accelerometers can be mitigated by optimizing the structural design and compensating the output signal. Herein, the output characteristics of an accelerometer designed with asymmetrically arranged combs were analyzed under various temperatures. The purpose of this paper is to improve the temperature drift of scale factor (TDSF) of MEMS capacitive accelerometer, using the asymmetric layout structure to improve the TDSF fundamentally, and the least square method to achieve temperature compensation efficiently. The variations in the TDSF were compared for the symmetric and asymmetric structures. In addition, we modeled the accelerometer with an asymmetric structure for simulations to analyze the errors resulting from the electrostatic torsion phenomenon induced by the asymmetric structure. Moreover, a temperature compensation model was developed for the scale factor of the accelerometer, which was validated and verified with the data obtained from simulations and experiment. Furthermore, an accelerometer based on silicon on insulator was fabricated and tested to verify the simulation results and the compensation effects. According to the results, the scale factor of the studied accelerometer was 171.83 mV g−1 and the average value of the TDSF was 83.56 ppm °C−1 Overall, the experimental results were almost consistent with the simulation results. Under the asymmetric layout, the scale-factor stability improvement of the accelerometer could reach up to 86.96%, and the error caused by electrostatic torsion was ∼2.93%, which is relatively negligible. After compensation, the range and standard deviation of the scale factor of the accelerometer with respect to temperature were reduced by 94.46% and 95.69%, respectively, and the average value of TDSF was reduced by 95.90%, which verified the effectiveness of the compensation model.

A Threshold Helium Leakage Detection Switch with Ultra Low Power Operation.

Detecting helium leakage is important in many applications, such as in dry cask nuclear waste storage systems. This work develops a helium detection system based on the relative permittivity (dielectric constant) difference between air and helium. This difference changes the status of an electrostatic microelectromechanical system (MEMS) switch. The switch is a capacitive-based device and requires a very negligible amount of power. Exciting the switch's electrical resonance enhances the MEMS switch sensitivity to detect low helium concentration. This work simulates two different MEMS switch configurations: a cantilever-based MEMS modeled as a single-degree-freedom model and a clamped-clamped beam MEMS molded using the COMSOL Multiphysics finite-element software. While both configurations demonstrate the switch's simple operation concept, the clamped-clamped beam was selected for detailed parametric characterization due to its comprehensive modeling approach. The beam detects at least 5% helium concentration levels when excited at 3.8 MHz, near electrical resonance. The switch performance decreases at lower excitation frequencies or increases the circuit resistance. The MEMS sensor detection level was relatively immune to beam thickness and parasitic capacitance changes. However, higher parasitic capacitance increases the switch's susceptibility to errors, fluctuations, and uncertainties.

Analyzing the effectiveness of MEMS sensor and IoT in predicting wave height using machine learning models

Wave height is a critical consideration in the planning and execution of maritime projects. Wave height forecasting methods include numerical and machine learning (ML) techniques. The traditional process involves using numerical wave prediction models, which are very successful but are highly complex as they require adequate information on nonlinear wind–wave and wave–wave interactions, such as the wave energy-balance equation. In contrast, ML techniques can predict wave height without prior knowledge of the above-mentioned complex interactions. This research aims to predict wave height using micro-electromechanical systems (MEMS), internet of things (IoTs), and ML-based approaches. A floating buoy is developed using a MEMS inertial measurement unit and an IoT microcontroller. An experiment is conducted in which the developed buoy is subjected to different wave heights in real time. The changes in three-axis acceleration and three-axis gyroscope signals are acquired by a computer via IoT. These signals are analyzed using ML-based classification models to accurately predict wave height. The obtained validation accuracy of the ML models K-NN (K-nearest neighbor), support vector machine, and the bagged tree is 0.9906, 0.9368, and 0.9887 respectively, which indicates that MEMS and IoT can be used to accurately classify and predict wave heights in real-time.

Field Test Research on the Downhole Multiphysics Micro-Measurer Based on the MEMS Microchip

Since pressure while drilling (PWD) has the disadvantages of single-point measurement and high cost of application, a micro-measurer based on MEMS (micro-electromechanical systems) sensor technology, which can measure downhole temperature, pressure, magnetic field, and dynamic signal, has been developed to achieve real-time, efficient, and accurate measurement of multiple parameters in the well. The kernel circuit system is the core of measurement and control, and the shell plays the role in protecting the kernel circuit. The shell of the micro-measurer is made of preferably selected materials with high-temperature and high-pressure resistance, corrosion resistance, small size, and low density, which can adapt to working in drilling fluid for a long time. The micro-measurer uses integrated interfaces on the shell to enable communication between the host computer and measuring machine. Based on the field test, both the functional integrity and data measurement accuracy of the micro-measurer are verified. Through analysis of the measured data, the profile of the downhole temperature field is constructed. The physical phenomena reflected by the measured magnetic field signal and dynamic signal are consistent with the actual working conditions observed in the test. Hence, as a new microchip measuring device, the micro-measurer can better serve the drilling engineering field and provide technical support for real-time measurement of downhole parameters in the future.

Linear Stiffness Tuning in MEMS Devices via Prestress Introduced by TiN Thin Films

Stiffness tuning is crucial for micro/nanoscale devices to achieve advanced functionalities such as bistability, which enables efficient energy harvesting, large motion actuation, and programmable mechanical properties. Prestress implemented by thin films is one of the effective approaches for stiffness control; however, it is rarely used in practice because of the poor controllability in traditional thin film materials. Here, we demonstrate robust stiffness tuning on a microelectromechanical system (MEMS) sensor by using prestress in titanium nitride (TiN) thin films. Benefiting from the atomic peening effect, TiN exhibits linearly controllable prestress, which provides direct access to zero and negative stiffness in the mechanical device, resulting in multiple functionalities, including significantly enhanced sensitivity near zero-stiffness and bistable snapping behavior as stiffness becomes negative. The demonstrated technique can be a powerful tool to manipulate mechanical behaviors and facilitate exceptional sensing and actuating functionalities for micro/nanoscale devices.

Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization

Micro-electromechanical systems (MEMS) technology-based sensors have found diverse fields of application due to the advancement in semiconductor manufacturing technology, which produces sensitive, low-cost, and powerful sensors. Due to the fabrication of different electrical and mechanical components on a single chip and complex process steps, MEMS sensors are prone to deterministic and random errors. Thus, testing, calibration, and quality control have become obligatory to maintain the quality and reliability of the sensors. This is where Artificial Intelligence (AI) can provide significant benefits, such as handling complex data, performing root cause analysis, efficient feature estimation, process optimization, product improvement, time-saving, automation, fault diagnosis and detection, drift compensation, signal de-noising, etc. Despite several benefits, the embodiment of AI poses multiple challenges. This review paper provides a systematic, in-depth analysis of AI applications in the MEMS-based sensors field for both the product and the system level adaptability by analyzing more than 100 articles. This paper summarizes the state-of-the-art, current trends of AI applications in MEMS sensors and outlines the challenges of AI incorporation in an industrial setting to improve manufacturing processes. Finally, we reflect upon all the findings based on the three proposed research questions to discover the future research scope.

Human Motion Capture Based on MEMS Sensor

In order to realize the monitoring of human joint rehabilitation, a human motion capture and recognition system is constructed by using micro electro mechanical system (MEMS) sensor nodes. A two-stage extended Kalman filter algorithm is proposed for multi-sensor data fusion. The error matrix between the coordinate system of sensor node and the coordinate system of body was calculated by using the stationary posture calibration. The root mean squared error (RMSE) of the computed joint angle time series is less than 0.5°. The feature of joint angle time series was extracted and the support vector machine (SVM) classification model based on particle swarm optimization (PSO) was established. The experimental results show that the SVM algorithm optimized by PSO has better recognition effect than BP neural network. The average recognition rate can reach more than 97%. The human motion capture system designed in this paper can effectively realize human motion capture, recognition and joint rehabilitation monitoring.