Wheatstone Bridge MEMS Hydrogen Sensor with ppb-Level Detection Limit Based on the Palladium-Gold Alloy.

On some occasions, such as new energy clinics, monitoring the trace hydrogen at the ppb level is necessary. The traditional resistive hydrogen sensors based on the Pd alloys are very difficult to realize such an extremely low detection limit. To achieve a detection limit at the ppb level and also ensure good stability, a MEMS hydrogen sensor was designed in a suspended Wheatstone bridge structure, with all four resistive arms defined on a sputtered Pd-Au alloy thin film. For the Wheatstone bridge sensor, absolute response (Ra) and relative response (Rs) are defined to describe the sensitivity of the sensor, and the effect of annealing temperature on baseline drift is investigated using the baseline zero drift parameter (DBZD). By testing the sensors across a hydrogen concentration range of 20 ppb to 3 v/v%, the optimal annealing temperature (250 °C) and operating temperature (60 °C) were identified. Under these conditions, the sensor exhibited a detection limit as low as 20 ppb with a power consumption of only 4.6 mW. At the same time, the response and recovery times of the sensor were 6 and 19 s, respectively, toward 3 v/v% hydrogen. After testing over a 100-day period, Ra fluctuated only 0.0026%, indicating that the hydrogen sensor had good long-term stability for low-concentration detection. More results also showed that the sensor has good repeatability, selectivity, and humidity resistance. With the wide measurement range (20 ppb to 3 v/v%), the sensor has the potential to meet hydrogen detection requirements in multiple scenarios.

Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors.

In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected.

Micromechanical Characterization of AlCu Films for MEMS Using Instrumented Indentation Method.

The micromechanical properties (i.e., hardness, elastic modulus, and stress-strain curve) of AlCu films were determined by an instrumented indentation test in this work. For three AlCu films with different thicknesses (i.e., 1 µm, 1.5 µm, and 2 µm), the same critical ratio (hmax/t) of 0.15 and relative indentation depth range of 0.15-0.5 existed, within which the elastic modulus (i.e., 59 GPa) and nanoindentation hardness (i.e., 0.75 GPa, 0.64 GPa and 0.63 GPa for 1 µm, 1.5 µm and 2 µm films) without pile-up and substrate influence can be determined. The yield strength (i.e., 0.754 GPa, 0.549 GPa and 0.471 GPa for 1 µm, 1.5 µm and 2 µm films) and hardening exponent (i.e., 0.073, 0.131 and 0.150 for 1 µm, 1.5 µm and 2 µm films) of Al-(4 wt.%)Cu films for MEMS were successfully reported for the first time using a nanoindentation reverse method. In dimensional analysis, the ideal representative strain εr was determined to be 0.038. The errors of residual depth hr between the simulations and the nanoindentation experiments was less than 5% when the stress-strain curve obtained by the nanoindentation reverse method was used for simulation.

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.

Variation of MEMS Thin Film Device Parameters under the Influence of Thermal Stresses.

With the advancement of semiconductor manufacturing technology, thin film structures were widely used in MEMS devices. These films played critical roles in providing support, reinforcement, and insulation in MEMS devices. However, due to their microscopic dimensions, the sensitivity of their parameters and performance to thermal stress increased significantly. In this study, a Pirani gauge sample with a multilayer thin film structure was designed and fabricated. Based on this sample, finite element modeling analysis and thermal stress experiments were conducted. The finite element modeling analysis employed a combination of steady-state and transient methods to simulate the deformation and stress distribution of the device at room temperature (25 °C), low temperature (-55 °C), and high temperature (125 °C). The thermal stress test involved placing the sample in a temperature cycling chamber for temperature cycling tests. After the tests, the resonant frequency and surface deformation of the device were measured to quantitatively evaluate the impact of thermal stress on the deformation and resonant frequency parameters of the device. After the experiments, it was found that the clamped-end beams made of Pt were a stress concentration area. Additionally, the repetitive thermal load caused the cantilever beam to move cyclically in the Z direction. This movement altered the deformation of the film and the resonant frequency. The suspended film exhibited concavity, and the overall trend of the resonant frequency was downward. Over time, this could even lead to the fracture of the clamped-end beams. The variation of mechanical parameters derived from finite element simulations and experiments provided an important reference value for device design improvement and played a crucial role in enhancing the reliability of thin film devices.

The Seven-State RF MEMS Miniaturized Broadband Reconfigurable Step Attenuator.

This paper presents a three-channel reconfigurable step attenuator based on radio frequency (RF) microelectromechanical system (MEMS) switches, in response to the current issues of high insertion loss and low attenuation accuracy of attenuators. The coplanar waveguide (CPW), cross-shaped power dividers, RF MEMS switches, and π-type attenuation resistor networks are designed as a basic unit of the attenuator. The attenuator implemented attenuation of 0~30 dB at 5 dB intervals in the frequency range of 1~25 GHz through two basic units. The results show that the insertion loss is less than 1.41 dB, the attenuation accuracy is better than 2.48 dB, and the geometric size is 2.4 mm × 4.0 mm × 0.7 mm. The attenuator can be applied to numerous fields such as radar, satellites, aerospace, electronic communication, and so on.

Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones.

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value.

A Polyimide Composite-Based Electromagnetic Cantilever Structure for Smart Grid Current Sensing.

Polyimides (PIs) have been extensively used in thin film and micro-electromechanical system (MEMS) processes based on their excellent thermal and mechanical stability and high glass transition temperature. This research explores the development of a novel multilayer and multifunctional polymer composite electro-piezomagnetic device that can function as an energy harvester or sensor for current-carrying wires or magnetic field sensing. The devices consist of four layers of composite materials with a polyimide matrix. The composites have various nanoparticles to alter the functionality of each layer. Nanoparticles of Ag were used to increase the electrical conductivity of polyimide and act as electrodes; lead zirconate titanate was used to make the piezoelectric composite layer; and either neodymium iron boron (NdFeB) or Terfenol-D was used to make the magnetic and magnetostrictive composite layer, which was used as the proof mass. A novel all-polymer multifunctional polyimide composite cantilever was developed to operate at low frequencies. This paper compares the performance of the different magnetic masses, shapes, and concentrations, as well as the development of an all-magnetostrictive device to detect voltage or current changes when coupled to the magnetic field from a current-carrying wire. The PI/PZT cantilever with the PI/NdFeB proof mass demonstrated higher voltage output compared to the PI/Terfenol-D proof mass device. However, the magnetostrictive composite film could be operated without a piezoelectric film based on the Villari effect, which consisted of a single PI-Terfenol-D film. The paper illustrates the potential to develop an all-polymer composite MEMS device capable of acting as a magnetic field or current sensor.

Research on Multiphysics-Driven MEMS Safety and Arming Devices.

As the core component of energy transfer in weapon system, safety and arming (S&A) devices affect the safety, reliability, and damage ability of the weapon. Micro-electromechanical systems (MEMS) S&A devices have been widely investigated for their smaller structure size, higher functional integration, and better smart functionality. This paper proposes the design of a multi-physics field-driven MEMS S&A device. The S&A mechanism is composed of a setback mechanism, a spin mechanism, and an electrothermal mechanism, achieving multiphysics-arming. With the coordination of the three mechanisms, the S&A device can produce a 1 mm displacement. The displacement generated allows the S&A device to switch between safety status and arming status. The unlock conditions and overload resistance of each mechanism are obtained by finite element simulation. Based on SOI wafers and silicon oxide wafers, the chips were fabricated and packaged. Several tests were carried out to verify the working condition and overload resistance of the S&A device. The result shows that under a voltage of 11 V and a rotation speed of 8000 r/min, with a size no more than 10 mm × 10 mm × 1.5 mm, the device works smoothly and can withstand an overload of 25,000 g.

Development of a Compact NDIR CO2 Gas Sensor for a Portable Gas Analyzer.

A carbon dioxide (CO2) gas sensor based on non-dispersive infrared (NDIR) technology has been developed and is suitable for use in portable devices for high-precision CO2 detection. The NDIR gas sensor comprises a MEMS infrared emitter, a MEMS thermopile detector with an integrated optical filter, and a compact gas cell with high optical coupling efficiency. A dual-ellipsoid mirror optical system was designed, and based on optical simulation analysis, the structure of the dual-ellipsoid reflective gas chamber was designed and optimized, achieving a coupling efficiency of up to 54%. Optical and thermal simulations were conducted to design the sensor structure, considering thermal management and light analysis. By optimizing the gas cell structure and conditioning circuit, we effectively reduced the sensor's baseline noise, enhancing the overall reliability and stability of the system. The sensor's dimensions were 20 mm × 10 mm × 4 mm (L × W × H), only 15% of the size of traditional NDIR gas sensors with equivalent detection resolution. The developed sensor offers high sensitivity and low noise, with a sensitivity of 15 µV/ppm, a detection limit of 90 ppm, and a resolution of 30 ppm. The total power consumption of the whole sensor system is 6.5 mW, with a maximum power consumption of only 90 mW.

When-to-Loop: Enhanced Loop Closure for LiDAR SLAM in Urban Environments Based on SCAN CONTEXT.

Global Navigation Satellite Systems (GNSSs) frequently encounter challenges in providing reliable navigation and positioning within urban canyons due to signal obstruction. Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMUs) offers an alternative for autonomous navigation, but they are susceptible to accumulating errors. To mitigate these influences, a LiDAR-based Simultaneous Localization and Mapping (SLAM) system is often employed. However, these systems face challenges in drift and error accumulation over time. This paper presents a novel approach to loop closure detection within LiDAR-based SLAM, focusing on the identification of previously visited locations to correct time-accumulated errors. Specifically, the proposed method leverages the vehicular drivable area and IMU trajectory to identify significant environmental changes in keyframe selection. This approach differs from conventional methods that only rely on distance or time intervals. Furthermore, the proposed method extends the SCAN CONTEXT algorithm. This technique incorporates the overall distribution of point clouds within a region rather than solely relying on maximum height to establish more robust loop closure constraints. Finally, the effectiveness of the proposed method is validated through experiments conducted on the KITTI dataset with an enhanced accuracy of 6%, and the local scenarios exhibit a remarkable improvement in accuracy of 17%, demonstrating improved robustness in loop closure detection for LiDAR-based SLAM.

Control of a Micro-Electro-Mechanical System Fast Steering Mirror with an Input Shaping Algorithm.

Fast steering mirrors (FSMs) designed by the micro-electro-mechanical system (MEMS) technology are significantly smaller in volume and mass, offering distinct advantages. To improve their performance in the open-loop control mode, this study introduces a control algorithm and evaluates its performance on an electromagnetic-driven MEMS-FSM. The algorithm employs a method to shape the input signal by fitting the system's transfer function and modifying the step response. This shaped signal is then applied to the system to minimize overshoot, reduce settling time, and improve working bandwidth, thereby enabling faster angular adjustments and improving the stability of the FSM. The experimental results demonstrate an 85.65% reduction in overshoot and a decrease in settling time from 84 ms to 0.4 ms. Consequently, the working bandwidth of the FSM system increases to 2500 Hz, demonstrating the effectiveness of the algorithm in enhancing MEMS-FSM's performance.

Ceramics for Microelectromechanical Systems Applications: A Review.

A comprehensive review of the application of different ceramics for MEMS devices is presented. Main ceramics materials used for MEMS systems and devices including alumina, zirconia, aluminum Nitride, Silicon Nitride, and LTCC are introduced. Conventional and new methods of fabricating each material are explained based on the literature, along with the advantages of the new approaches, mainly additive manufacturing, i.e., 3D-printing technologies. Various manufacturing processes with relevant sub-techniques are detailed and the ones that are more suitable to have an application for MEMS devices are highlighted with their properties. In the main body of this paper, each material with its application for MEMS is categorized and explained. The majority of works are within three main classifications, including the following: (i) using ceramics as a substrate for MEMS devices to be mounted or fabricated on top of it; (ii) ceramics are a part of the materials used for an MEMS device or a monolithic fabrication of MEMS and ceramics; and finally, (iii) using ceramics as packaging solution for MEMS devices. We elaborate on how ceramics may be superior substitutes over other materials when delicate MEMS-based systems need to be assembled or packaged by a simpler fabrication process as well as their advantages when they need to operate in harsh environments.

Quasi-Optical Four-Port Acoustic Filters Based on NEMS Coupled Beam Arrays.

Theoretical models are presented for quasi-optical four-port acoustic devices based on NEMS-coupled beam arrays. Analogies with coupled mode devices in microwaves, ultrasonics, optics, and electron wave optics are first reviewed, together with coupled beam filters. Power transfer between two mechanically coupled, electrostatically driven, coupled beam arrays is then demonstrated using a lumped element model, and the conditions for full power transfer are established. Four-port devices, including directional couplers and coupler filters with complementary transmission ports, are then demonstrated. Predictions are verified for realistic device layouts using the stiffness matrix method.

Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person's psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.

Noise Analysis and Suppression Methods for the Front-End Readout Circuit of a Microelectromechanical Systems Gyroscope.

Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. First, the noise analysis of the front-end readout circuit is carried out with theoretical derivation to clarify the main noise contributors. To suppress the output noise, an improved readout circuit based on the T-resistor networks is proposed, and the corresponding noise equation is derived in detail. In addition, the noise analysis of the critical circuits of the detection and control system, such as the inverting amplifiers, the first-order low-pass filters, and the first-order high-pass filters, is carried out, and the noise suppression strategy with the optimization of the resistances and is proposed. Taking the inverting amplifier as an example, the theoretical derivation is verified by measuring and comparing the output noises of different resistance schemes. In addition, the output noises of the gyroscope before and after circuit optimization are measured. Experimental results demonstrate that the output noise with the circuit optimization is reduced from 60 µV/Hz1/2 to 30 µV/Hz1/2 and the bias instability is reduced from 3.8 deg/h to 1.38 deg/h. In addition, the ARW is significantly improved from 0.035 deg/h1/2 to 0.018 deg/h1/2, which indicates that the proposed noise analysis and suppression methods are effective and feasible.

Multianalyte Detection with Metasurface-Based Midinfrared Microspectrometer.

Midinfrared (2.5-25 µm) spectroscopy is an ideal tool for identifying chemicals in a nondestructive manner. The traditional platform is a Fourier transform infrared (FTIR) spectrometer, but this is too bulky, expensive, and power-hungry for many applications. There is therefore a growing demand for small, lightweight, and cost-effective microspectrometers for use in the field. One emerging platform is the filter-array detector-array microspectrometer. It pairs a broadband detector array with a thin and rigid array of spectral filters to offer a robust, compact platform for real-time in situ sensing. However, most demonstrations have only focused on identifying a single chemical against a null sample, even though many applications would involve multianalyte detection. In this work, we show a rare attempt at simultaneously tracking multiple analytes with a metasurface filter-array microspectrometer. The metasurface consists of periodic lattices of subwavelength circular apertures in an aluminum layer to create an array of bandpass filters. The filter array is imaged with an off-the-shelf microbolometer via a reverse-lens imaging setup to simultaneously monitor the concentration of ethanol and methanol in gasoline. This represents an important application of fuel quality monitoring. Chemometric models (PLS and SVR) are trained and tested on gasoline blends with ethanol and methanol contents, both ranging from 0% to 20% v/v. A support vector machine regression (SVR) model with a cubic kernel was found to have the lowest combined prediction errors. The root-mean-square-error of prediction (RMSEP) for ethanol and methanol are 1.23% and 1.84% v/v; the corresponding pseudounivariate limit of detection is found to be 4.22% and 6.86% v/v, respectively. This work takes the emerging field of metasurface-based mid-infrared spectrometers from single- to multianalyte detection, thereby considerably expanding their range of potential applications.

New design of operational MEMS bridges for measurements of properties of FEBID-based nanostructures.

Focused electron beam-induced deposition (FEBID) is a novel technique for the development of multimaterial nanostructures. More importantly, it is applicable to the fabrication of free-standing nanostructures. Experimenting at the nanoscale requires instruments with sufficient resolution and sensitivity to measure various properties of nanostructures. Such measurements (regardless of the nature of the quantities being measured) are particularly problematic in the case of free-standing nanostructures, whose properties must be separated from the measurement system to avoid possible interference. In this paper, we propose novel devices, namely operational micro-electromechanical system (opMEMS) bridges. These are 3D substrates with nanometer-scale actuation capability and equipped with electrical contacts characterised by leakage resistances above 100 GΩ, which provide a platform for comprehensive measurements of properties (i.e., resistance) of free-standing FEBID structures. We also present a use case scenario in which an opMEMS bridge is used to measure the resistance of a free-standing FEBID nanostructure.

Inhibiting Emulative Oxygen Adsorption via Introducing Pt-Segregated Sites into the Pd Surface for Enhanced H2 Sensing in Air.

Pd-modified metal sulfide gas sensors exhibit excellent hydrogen (H2) sensing activity through spillover effects. However, the emulative oxygen adsorption often occupies an exposed Pd surface and thus limits the effective Pd-H interaction, impeding the H2 sensing performance in air. Herein, we develop an edge-rich Pt-shell/Pd-core structure to adjust the selective adsorption between oxygen and hydrogen for effective H2 sensing in an air atmosphere. Detailedly, through accurately regulating the rate of Pt deposition onto the icosahedron Pd surface, an edge-rich Pt-shell/Pd-core structure can be first achieved. It has been found that marginal Pt aggregations can segregate the oxygen molecules around the Pt species and induce easier Pt-O bonding, further guiding accessible Pd surfaces for effective Pd-H interactions, which can be verified by 1H ssNMR, in-situ Raman, ex-situ XPS, and density functional theory analyses. The final ZnS/PdPt sensor exhibits an ultrasensitive response (8608 to 4% H2) and a wide detected range (0.5 ppm-4%) in air, exceeding most reported hydrogen sensors.

Analyzing the Maharashtra Ambulance Service "108": The Prospect and Challenges.

The utilization of digital health in India is playing a crucial role in enhancing healthcare services by transitioning from the current inadequate public health structure to a more efficient and patient-centric system. Digital health includes various digital tools, such as electronic health records (EHRs), telemedicine, mobile health applications, health information exchange systems, and other technological advancements to improve access, efficiency, and quality of healthcare delivery. This study investigates the prospects and challenges encountered by the newly-digitized Maharashtra Emergency Medical Services (MEMS). Utilizing the 38,823 MEMS calls from November 2022, this study investigates the current status of emergency service delivery mechanisms in Maharashtra. Through spatial analyses, this study also explores the causes behind calls. The findings of the study show that calls for 108 ambulance services were distributed across the districts and had variable service delivery time periods. Current challenges to the system arise from various urban and healthcare infrastructure problems, as well as socio-cultural challenges. Implementation of the digitized MEMS system reveals key factors that influence the service's success, assisting the policymakers and health administrators in identifying and further improving the service.