MEMS Sensors Research Articles

A Single MEMS Sensor Based on SnO₂ Nanosheets for Selective Gas Identification

A Single MEMS Sensor Based on SnO₂ Nanosheets for Selective Gas Identification

IOT Based Mining, Tracking and Worker Safety Helmet

Abstract: This project addresses a cost-effective, flexible solution of underground mine workers safety. A module of MEMS and temperature-based sensors are used for underground environment monitoring and automating progression of measurement data through IOT communication technique is proposed with high accuracy, smooth control and reliability. Arduino microcontroller is used for collecting data and making decision, based on which the mine worker is informed through server. Sensor data transforms into digital signal and effectively communicate wirelessly with the ground control centre computer.

Retraction Note: Big data tracking and automatic measurement technology for unmanned aerial vehicle trajectory based on MEMS sensor

Retraction Note: Big data tracking and automatic measurement technology for unmanned aerial vehicle trajectory based on MEMS sensor

Piezo-VFETs: Vacuum Field Emission Transistors Controlled by Piezoelectric MEMS Sensors as an Artificial Mechanoreceptor with High Sensitivity and Low Power Consumption.

As one of the most promising electronic devices in the post-Moore era, nanoscale vacuum field emission transistors (VFETs) have garnered significant attention due to their unique electron transport mechanism featuring ballistic transport within vacuum channels. Existing research on these nanoscale vacuum channel devices has primarily focused on structural design for logic circuits. Studies exploring their application potential in other vital fields, such as sensors based on VFET, are more limited. In this study, for the first time, the design of a vacuum field emission transistor (VFET) coupled with a piezoelectric microelectromechanical (MEMS) sensing unit is proposed as the artificial mechanoreceptor for sensing purposes. With a negative threshold voltage similar to an N-channel depletion-mode metal oxide silicon field effect transistor, the proposed VFET has its continuous current tuned by the piezoelectric potential generated by the sensing unit, amplifying the magnitude of signals resulting from electromechanical coupling. Simulations have been conducted to validate the feasibility of such a configuration. As indictable from the simulation results, the proposed piezoelectric VFET exhibits high sensitivity and an electrically adjustable measurement range. Compared to the traditional combination of piezoelectric MEMS sensors and solid-state field effect transistors (FETs), the piezoelectric VFET design has a significantly reduced power consumption thanks to its continuous current that is orders of magnitude smaller. These findings reveal the immense potential of piezoelectric VFET in sensing applications, building up the basis for using VFETs for simple, effective, and low-power pre-amplification of piezoelectric MEMS sensors and broadening the application scope of VFET in general.

Development of low-cost MEMS microphone array for sound localization in urban environments

Understanding noise pollution and mitigating its impact on the quality of life in urban environment is a major challenge from a societal and health point of view. In order to monitor and ultimately understand these noisy environments a process of long term acoustic measurements and analysis is required. These urban environments are noisy and obtaining a clear recording of a desired acoustic signal is difficult hence an effective approach is the use of beamforming theory coupled with a microphone array. In this paper a novel approach utilizing compact low-cost microphone arrays equipped with MEMS sensors for continuous sound localization in urban areas is proposed. This approach focuses on leveraging the affordability and scalability of MEMS sensors to deploy distributed acoustic sensor networks across diverse urban locations. The aim is to capture real-time acoustic data, enabling comprehensive monitoring of sound sources and their spatial distribution within the urban environment. This sensor unit can be upgraded with a camera, i.e. acoustic camera, which will provide information on the conditions of the environment where the measurements are performed. This low-cost solution facilitates long-term monitoring, providing valuable insights into the dynamic nature of urban soundscapes.

Nitrogen-Doped Carbon Quantum Dots Activated Dandelion-Like Hierarchical WO3 for Highly Sensitive and Selective MEMS Sensors in Diabetes Detection.

High sensitivity, low concentration, and excellent selectivity are pronounced primary challenges for semiconductor gas sensors to monitor acetone from exhaled breath. In this study, nitrogen-doped carbon quantum dots (N-CQDs) with high reactivity were used to activate dandelion-like hierarchical tungsten oxide (WO3) microspheres to construct an efficient and stable acetone gas sensor. Benefiting from the synergistic effect of both the abundant active sites provided by the unique dandelion-like hierarchical structure and the high reaction potential generated by the sensitization of the N-CQDs, the resulting 16 wt % N-CQDs/WO3 sensor shows an ultrahigh response value (Ra/Rg = 74@1 ppm acetone), low detection limit (0.05 ppm), outstanding selectivity, and reliable stability to acetone at the optimum working temperature of 210 °C. Noteworthy that the N-CQDs facilitate the carrier migration and intensify the reaction between acetone and WO3 during the sensing process. Considering the above advantages, N-CQDs as a sensitizer to achieve excellent gas-sensitive properties of WO3 are a promising new strategy for achieving accurate acetone detection in real time and facilitating the development of portable human-exhaled gas sensors.

Integrating low-cost GNSS and MEMS accelerometer for precise dynamic displacement monitoring

We assess the performance of the self-developed receiver coupling dual-frequency low-cost GNSS chipset and IMU MEMS sensor. The system also comprises software dedicated to vibration monitoring based on relative kinematic positioning in a post-processing mode. The observation assessment reveals high accuracy of low-cost GNSS and MEMS accelerometer data. The Septentrio Mosaic X5 chipset exhibits competitive to high-grade receivers’ phase observation noise, outperformance in code noise level and observation auto-correlation at a level that may be neglected even in high-rate applications. The evaluation of the MEMS accelerometer data proves that the sensor accuracy is adequate for vibration monitoring. With the shake table experiment, we examine the performance of vibration recovery. An advantage of the coupled solution over a GNSS-only one is documented with a 15 % reduction of the amplitude error. The backward smoothing of coupled solutions drives the next 20 % error drop. Finally, the high performance of the coupled solution based on low-cost GNSS & accelerometer is confirmed by a mean amplitude error of 0.4 mm. The frequency analyses indicate that both coupled and single-sensor solutions precisely detect the vibration frequencies; however, they also confirm the outperformance of the former over the latter. The power spectra of the recovered displacement time series accentuate the benefits of GNSS + accelerometer fusion and backward filtering for reducing displacement noise.

Integrated Amorphous Carbon Film Temperature Sensor with Silicon Accelerometer into MEMS Sensor.

Amorphous carbon (a-C) has promising potential for temperature sensing due to its outstanding properties. In this work, an a-C thin film temperature sensor integrated with the MEMS silicon accelerometer was proposed, and a-C film was deposited on the fixed frame of the accelerometer chip. The a-C film was deposited by DC magnetron sputtering and linear ion beam, respectively. The nanostructures of two types of films were observed by SEM and TEM. The cluster size of sp2 was analyzed by Raman, and the content of sp2 and sp3 of the carbon film was analyzed by XPS. It showed that the DC-sputtered amorphous carbon film, which had a higher sp2 content, had better temperature-sensitive properties. Then, an integrated sensor chip was designed, and the structure of the accelerometer was simulated and optimized to determine the final sizes. The temperature sensor module had a sensitivity of 1.62 mV/°C at the input voltage of 5 V with a linearity of 0.9958 in the temperature range of 20~150 °C. The sensitivity of the sensor is slightly higher than that of traditional metal film temperature sensors. The accelerometer module had a sensitivity of 1.4 mV/g/5 V, a nonlinearity of 0.38%, a repeatability of 1.56%, a total thermomechanical noise of 509 μg over the range of 1 to 20 Hz, and an average thermomechanical noise density of 116 µg/√Hz, which is smaller than the input acceleration amplitude for testing sensitivity. Under different temperatures, the performance of the accelerometer was tested. This research provided significant insights into the convenient procedure to develop a high-performance, economical temperature-accelerometer-integrated MEMS sensor.

An amplitude-based temperature compensated MEMS resonant pressure sensor with single resonator

Temperature compensation is a common concern of MEMS sensors. This paper presents an amplitude-based temperature compensated MEMS resonant pressure sensor. An AGC (automatic gain control) closed-loop circuit with a constant-amplitude drive was designed, enabling the real-time sampling of resonant frequencies and amplitudes. Under the constant-amplitude drive, the amplitude of the resonator changes monotonously with temperature. Thus, pressure and temperature can be decoupled using a single resonator, achieving high accuracy in pressure measurements. Experimental results demonstrated that the fabricated microsensor achieved high performance with the amplitude-based temperature compensation. Within a pressure range of 10 kPa to 100 kPa and a temperature range of −20℃ to 60℃, the pressure accuracy of the microsensor reached ± 0.012 %FS (full scale), with a repeatability error of 0.009 %FS and a pressure hysteresis error of 0.007 %FS. Based on single resonator, the developed microsensor is expected to reduce device sizes, simplify design and fabrication processes, and decrease the power consumption of the system.

High frequency venting of MEMS ultrasonic transducers and sensors: materials solutions.

Abstract This paper introduces the concept of ultrasonic venting. Similar to acoustic vents, ultrasonic vents refer to the aperture in air-coupled MEMS ultrasonic transducers (either PMUT or CMUT) intended to allow the equalization of internal and external pressures, the transfer of heat and the pass of ultrasonic waves, while impeding the penetration of fluids or particles that can affect the transducer membrane. To that end, vents are covered with a porous membrane whose properties are tuned to meet the afore mentioned requirements. The main difficulty in ultrasonic venting, compared with acoustic venting, is that the required “transparency” to ultrasonic waves is much more difficult to achieve. This involves two main problems as both transmission loss and frequency distortion are much larger at ultrasonic frequencies than in the audio range. The objectives of this paper are: to measure the response of acoustic venting materials in the ultrasonic frequency range, to determine the usability of these materials in ultrasonic vents, and to extract useful information for the design of efficient ultrasonic venting materials. Transmission coefficient spectra of different acoustic venting materials is measured in the frequency range 0.2 –2.7 MHz. The origin of the ultrasonic losses and frequency distortion are analysed as well as the role of mode conversion, internal interferences, modes interference, etc. Results reveal that none of the acoustic venting materials analysed can be used in ultrasonic venting applications, but the obtained knowledge about the response of these materials in the ultrasonic frequency range permit to advance in the selection of successful candidate materials for this application.

Discrimination of VOCs with e-nose consisting of a single MEMS sensor using lightweight deep learning model based on SqueezeNet

The detection of volatile organic compounds (VOCs) has numerous applications in environmental monitoring and biomedicine. Metal oxide semiconductor (MOS) gas sensors are widely used in gas detection due to their simple structure, low fabrication cost, and fast response speed. However, MOS gas sensors have broad-spectrum responsiveness to VOCs, making selectivity a challenge. The most commonly used method to obtain selectivity is the E-nose system consisting of multiple sensors. However, the size of E-noses has increased due to the number of sensors with high power consumption and complicated signal processing. In recent years, deep learning has flourished, allowing for accurate discrimination of VOCs and concentration prediction using E-nose consisting of a single sensor. This method significantly reduces power consumption and facilitates portable applications. In this work, we synthesize SnO2-ZnO composite oxide and fabricate a E-nose consisting of a single MEMS gas sensor. The response performance of the E-nose to VOCs is stable with a high output signal-noise ratio (SNR). We design a lightweight deep learning model based on SqueezeNet and train it using transfer learning. Linear regression is used to predict the concentration of VOCs. Our work achieves accurate identification and low-error concentration prediction of VOCs, which is valuable for subsequent portable VOC detection systems.

Research on Spatial Electric Field Measurement of DC Line with MEMS Electric Field Sensor

Abstract As more and more UHVDC transmission lines are put into operation or under construction, the demand for space electromagnetic environment monitoring around transmission lines is increasingly urgent. In view of the lack of effective measuring instruments and methods for spatial DC electric field, this paper studies the spatial electric field measurement of DC line with a field mills MEMS electric field sensor. The relationship between the output current of the sensor and the intensity of the DC electric field is derived. In view of the error caused by the space accumulation of induced charge on the cover of the sensor, the structure of the sensor is further designed. In the new structure design, the shielding electrode grounding method is used to solve the accumulation of space charge on the shielding sheet. After calibrating and testing the response characteristics of the sensor in the performance test platform, the sensor is used in the DC line space electric field simulation test experiment. The output results of MEMS sensor at different spacing and voltage levels are consistent with the electric field distribution law. The measurement results prove that the designed structure is feasible to measure the space electric field of DC line, which is important for monitoring the electromagnetic environment.

Dual-Functional Cross-Meandering Resonator for Power Frequency Electromagnetic Shielding and Wireless Sensing Communication.

The research on MEMS wireless sensing technology adapted to strong power frequency electromagnetic field environments is of great significance to our energy security, economic society, and even national security. Here, we propose a subwavelength cross-meandering resonator (0.49λ0 × 0.49λ0) to simultaneously achieve power frequency electromagnetic field shielding and wireless communication signal transmission. The element size of the resonator is only λ0/11, which is much smaller than that of previous works. In the resonator, a resonance mode with the significant near-field enhancement effect (about 180 times that at f = 1 GHz) is supported. Based on the self-made shielding box experimental setup, the measured shielding effectiveness of the resonator sample can reach more than 33 dB. Moreover, by integrating the cross-meandering resonator with the MEMS sensor, a wireless communication signal can be successfully transmitted. A dual-function cross-meandering resonator integrated with sensors may find potential applications in many military and civilian industries associated with strong power frequency electromagnetic fields.

Vibration Analysis at Castello Ursino Picture Gallery (Sicily, Italy) for the Implementation of Self-Generating AlN-MEMS Sensors.

This work explores the potential of self-powered MEMS devices for application in the preventive conservation of cultural heritage. The main objective is to evaluate the effectiveness of piezoelectric aluminum nitride MEMS (AlN-MEMS) for monitoring vibrations and to investigate its potential for harvesting energy from vibrations, including those induced by visitors. A preliminary laboratory comparison was conducted between AlN-MEMS and the commercial device Tromino®. The study was then extended to the Picture Gallery of Ursino Castle, where joint measurements with the two devices were carried out. The analysis focused on identifying natural frequencies and vibrational energy levels by key metrics, including spectral peaks and the Power Spectral Density (PSD). The results indicated that the response of the AlN-MEMS aligned well with the data collected by the commercial device, especially observing high vibrational energy around 100 Hz. Such results validate the potential of AlN-MEMS for effective vibration measurement and for converting kinetic energy into electrical power, thereby eliminating the need for external power sources. Additionally, the vibrational analysis highlighted specific locations, such as the measurement point Cu4, as exhibiting the highest vibrational energy levels. These points could be used for placing MEMS sensors to ensure efficient vibration monitoring and energy harvesting.

Neural network-assisted emission spectra analysis of gases using MEMS sensor: Predicting chemical composition and pressure

The paper explores the potential use of neural networks to predict the chemical composition as well as the total and partial pressure of individual gases in the mixture based on their emission spectra. Excitation/ionization of gas particles is ensured by a miniature MEMS (micro-electro-mechanical) sensor. The impact of measurement conditions on the generated spectra and possible challenges in analysis are addressed. The trained neural networks achieved a mean absolute error of approximately 1.4% in determining the chemical composition of a mixture of methane and hydrogen and 0.8% for a mixture of nitrogen and oxygen. For pressure prediction, spanning several orders of magnitude, a general model predicting pressure for ten different gases achieved a relative mean absolute error of 4.5%. Additionally, a model for gas classification achieved an accuracy of over 99%.

Performance analysis of FEM simulated different shaped membranes based capacitive MEMS sensor

Performance analysis of FEM simulated different shaped membranes based capacitive MEMS sensor

Design Considerations of Implantable MEMS Sensor for Bladder Pressure Monitoring: A Review

One of the main urinary disorders that impairs the bladder's capacity to regulate urine flow is urinary incontinence (UI). Currently, the new development of wireless implantable MEMS sensors has been studied to replace current clinical test of bladder pressure. This new invention grants a long-term pressure monitoring within the bladder, less painful with non-surgical, reducing the potential of infection thus maintains the patient’s quality of life. However, developing implanted BioMEMS sensors pose challenging tasks due to various constraint requirements that need to be fulfilled such as appropriate measurement ranges and bandwidth, suitable transduction mechanism, biocompatible materials, structural shape and size as well as wireless telemetry capability. This paper presents a review on implantable MEMS sensors and design challenges specifically for bladder pressure monitoring application.

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement.

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements.

Simulation research on Tai Chi movement posture resolution based on multi-MEMS sensor combination

The combined system based on multiple MEMS sensors is a miniature measurement system used for dynamic output and display of 3D information about the user's posture. It is mainly used for various Tai Chi movement posture calculation simulation research, wearable devices, etc. This article explores MEMS sensor technology, focusing on MEMS sensor data processing, Tai Chi movement position calculation and fusion calculation positioning algorithm. Due to the high noise characteristics of MEMS sensor devices, time series analysis is used to model MIMU signals and Kalman filtering is optimized. As a research field, simulation of Tai Chi movement appears in the intersection of biomechanics, robotics and computer science. The purpose is to create a computer model to simulate the natural and real body movements of the human body under certain conditions. In addition to creating special effects, Tai Chi movement posture calculation simulation can also be used for operation training and research on body structure. This article first introduces the typical applications of several MEMS sensor combinations, and then introduces the key technology of studying Tai Chi movement simulation. The kinematics and mechanics data of Tai Chi are obtained using biomechanical measurement technology, while the individual simulation of Tai Chi dynamics is realized in a certain mode of the machine. By creating a kinematic model of the human upper limb, and finally creating a flexible machine that imitates the human upper limb, to analyze the kinematic characteristics of the human upper limb, and cleverly realize the imitation of active interaction, the simulation of human movement and the solution of Tai Chi movement posture Simulation.

Modeling and Design Parameter Optimization to Improve the Sensitivity of a Bimorph Polysilicon-Based MEMS Sensor for Helium Detection.

Helium is integral in several industries, including nuclear waste management and semiconductors. Thus, developing a sensing method for detecting helium is essential to ensure the proper operation of such facilities. Several approaches can be used for helium detection, including based on the high thermal conductivity of helium, which is several times higher than air. This work utilizes the high thermal conductivity of helium to design and analyze a bimorph MEMS sensor for helium sensing applications. COMSOL Multiphysics software (version 6.2) is used to carry out this investigation. The sensor is constructed from poly-silicon and SiO2 materials with a trenched cantilever beam configuration. The sensor is electrically heated, and its morphed displacement depends on the surrounding gas's composition, which decreases in the presence of helium. Several factors were investigated to probe their effect on the sensor's sensitivity to helium, including the thickness of the poly-silicon layer, the configuration of the trench, and the thickness and location of SiO2 layer. The simulations showed that the best performance, up to 2 ppm helium detection level, can be achieved with thinner beams and medium trench lengths.