Search Results (726)

The weighted averaging algorithm is a widely adopted high-efficiency data fusion approach for micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) array, where the configuration of weighting coefficients plays a critical role in improving measurement accuracy. In this study, an optimal weighted averaging algorithm based on the fruit fly optimization algorithm (FOA) is proposed by analyzing the data fusion mechanism of the MEMS IMU array. Firstly, a measurement model for the MEMS IMU array is constructed, and the principles of data fusion are systematically investigated. Secondly, the optimal weighting coefficients under ideal conditions are derived, and their limitations in practical applications are discussed. Building on this framework, the FOA is employed to search for optimal weights, enabling the realization of high-precision weighted averaging fusion. Simulation and experimental results demonstrate that the proposed method outperforms conventional approaches in terms of accuracy and robustness. Full article

This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article

Microfluidic systems have become a hot topic in Micro-Electro-Mechanical System (MEMS) research, with micropumps serving as a key element due to their role in determining structural and flow dynamics within these systems. This study aims to analyze the influence of different structural obstacles within microfluidics on micropump efficiency and offer guidance for improving microfluidic system designs. In this context, a MEMS-based micropump valve structure was developed, and simulations were conducted to examine the effects of the valve on microfluidic oscillations. The research explored various configurations, including valve positions and quantities, yielding valuable insights for optimizing microfluidic transport mechanisms at the microscale. Full article

The importance and challenges of ethylene detection based on combustion-type low-cost commercial sensors for agricultural and industrial applications are well-established. This work summarizes the significant progress in ethylene detection based on an innovative Gas Metal Oxide Semiconductor (GMOS) sensor and a new catalytic composition of metallic nanoparticles. The paper presents a study on ethylene and ethanol sensing using a miniature catalytic sensor fabricated by Complementary Metal Oxide Semiconductor–Silicon-on-Insulator–Micro-Electro-Mechanical System (CMOS-SOI-MEMS) technology. The GMOS performance with bimetallic palladium–platinum (Pd-Pt) and monometallic palladium (Pd) and platinum (Pt) catalysts is compared. The synergetic effect of the Pd-Pt catalyst is observed, which is expressed in the shift of combustion reaction ignition to lower catalyst temperatures as well as increased sensitivity compared to monometallic components. The optimal catalysts and their temperature regimes for low and high ethylene concentrations are chosen, resulting in lower power consumption by the sensor. Full article

Micro-electro-mechanical system (MEMS) electromagnetic actuators have rapidly evolved into critical components of various microscale applications, offering significant advantages including precision, controllability, high force density, and rapid responsiveness. Recent advancements in actuator design, fabrication methodologies, smart control integration, and emerging application domains have significantly broadened their capabilities and practical applications. This comprehensive review systematically analyzes the recent developments in MEMS electromagnetic actuators, highlighting core operating principles such as Lorentz force and magnetic attraction/repulsion mechanisms and examining state-of-the-art fabrication technologies, such as advanced microfabrication techniques, additive manufacturing, and innovative material applications. Additionally, we provide an in-depth discussion on recent enhancements in actuator performance through smart and adaptive integration strategies, focusing on improved reliability, accuracy, and dynamic responsiveness. Emerging application fields, particularly micro-optical systems, microrobotics, precision micromanipulation, and microfluidic components, are extensively explored, demonstrating how recent innovations have significantly impacted these sectors. Finally, critical challenges, including miniaturization constraints, integration complexities, power efficiency, and reliability issues, are identified, alongside a prospective outlook outlining promising future research directions. This review aims to serve as an authoritative resource, fostering further innovation and technological advancement in MEMS actuators and related interdisciplinary fields. Full article

Micro-Electro-Mechanical Systems (MEMS) loudspeakers are attracting growing interest as alternatives to conventional miniature transducers for in-ear audio applications. However, their practical deployment is often hindered by pronounced resonances in their frequency response, caused by the mechanical and acoustic characteristics of the device structure. To mitigate these limitations, we present a model-based digital signal equalization approach that leverages a circuit equivalent model of the considered MEMS loudspeaker. The method relies on constructing an inverse circuital model based on the nullor, which is implemented in the discrete-time domain using Wave Digital Filters (WDFs). This inverse system is employed to pre-process the input voltage signal, effectively compensating for the transducer frequency response. The experimental results demonstrate that the proposed method significantly flattens the Sound Pressure Level (SPL) over the 100 Hz-10 kHz frequency range, with a maximum deviation from the target flat frequency response of below 5 dB. Full article

In this paper, we proposed an eye-tracking system featuring a small size and high scanning frequency, utilizing an electrostatic biaxial scanning mirror fabricated through a micro-electro-mechanical system (MEMS) process. A laser beam is directed onto the mirror, and the two axes of the mirror generate a Lissajous scanning pattern within an artificial eyeball. The scanning pattern reflected from the eyeball is detected by a linear photodiode sensor array (LPSA). The direction and rotation angle of the artificial eyeball result in varying grayscale values across a series of pixels detected by the LPSA, in which the average grayscale values change accordingly. By performing a linear fit between different rotation angles of the same eye movement direction and the corresponding grayscale values, we can determine the correlation between the direction of eye movement and the signal magnitude received by the LPSA, thereby enabling precise eye tracking. The results demonstrated that the minimum resolution was 0.6°. This preliminary result indicates that the system has good accuracy. In the future, this eye-tracking system can be integrated into various wearable glasses devices and applied in various fields, including medicine and psychology. Full article

To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. To enhance predictive accuracy of water trace, spectral data were preprocessed using smoothing combined with first-derivative processing, and optimal selection of absorption wavelength feature was performed using interval Partial Least Squares (iPLS). Cross-batch external validation demonstrated a Limit of Detection (LOD) of 0.026% with 95% confidence which satisfies the rapid screening requirements for water exceedances (>0.1%) in industrial applications. These findings provide a robust technical foundation for developing handheld, in situ water detection devices. Full article

Microsystem technology (MST) and micro-electro-mechanical systems (MEMS) are key technologies that continually introduce new application opportunities. Increasing complexity and individualization require systematic process development to avoid errors and delays. While existing methods for process development address various aspects of the manufacturing process, the systematic consideration of external factors influencing the process environment (PEnv) remains broadly inadequate. Despite extensive standards, PEnv-related influences lead to quality fluctuations in practice. A list of influencing factors and an example process illustrate these challenges. This study aims to analyze which methods exist for process development in MST and to what extent they systematically consider process environmental factors. A mixed methods design was used for the analysis. In a systematic literature review (SLR) using traditional databases and Artificial intelligence-supported search tools, a total of 75 relevant studies from the years 2005 to 2024 were identified. The methods that cover various aspects of process development are presented in an overview. An adapted GRADE (Grading of Recommendations Assessment, Development, and Evaluation) analysis was used to check the extent to which the PEnv can be included in process development using the methods currently available. The results show that existing approaches often take PEnv into account insufficiently. Efficient consideration with the use of current methods requires extensive expert knowledge, knowledge management, and project-specific supplementary methods. This study emphasizes the need for research into methods that systematically integrate environmental requirements into process development to improve the efficiency and quality of MST manufacturing in this area. Full article

This paper presents an ultrasonic driving and detection system based on a CMUT array using MEMS technology. Among them, the core component CMUT array is composed of 8 × 8 CMUT array elements, and each CMUT array element contains 6 × 6 CMUT units. The collapse voltage of a single CMUT unit obtained through finite element analysis is 95.91 V, and the resonant frequency is 3.16 MHz. The driving section achieves 64-channel synchronous driving, with key parameters including an adjustable excitation signal frequency ranging from 10 kHz to 5.71 MHz, a delay precision of up to 1 ns, and an excitation duration of eight pulse cycles. For the echo reception, a two-stage amplification circuit for high-frequency weak echoes with 32 channels was designed, achieving a gain of 113.72 dB and −3 dB bandwidth of 3.89 MHz. Simultaneously, a 32-channel analog-to-digital conversion based on a self-calibration algorithm was implemented, with a sampling rate of 50 Mbps and a data width of 10 bits. Finally, the experimental results confirm the successful implementation of the driving system’s designed functions, yielding a center frequency of 1.4995 MHz and a relative bandwidth of 127.9%@−6 dB for the CMUT operating in silicone oil. This paper successfully conducted the transmit–receive integrated experiment of the CMUT and applied Butterworth filtering to the echo data, resulting in high-quality ultrasonic echo signals that validate the applicability of the designed CMUT-based system for ultrasonic imaging. Full article

Accurate predictions of elastic properties under varying doping concentrations and temperatures are critical for designing reliable silicon-based micro-/nano-electro-mechanical systems (MEMS/NEMS). Empirical potentials typically lack accuracy for elastic predictions, whereas density functional theory (DFT) calculations are precise but computationally expensive. In this study, we developed a highly accurate and efficient machine learning-based Deep Potential (DP) model to predict the elastic constants of phosphorus-doped silicon (Si_64−xP_x, x = 0, 1, 2, 3, 4) within a temperature range of 0–500 K. The DP model was rigorously validated against benchmark DFT results. At 0 K, the elastic constants predicted by our DP model exhibited excellent agreement with experimental data, achieving a mean absolute percentage error (MAPE) of only 2.88%. We investigated the effects of doping on elastic constants in single-crystal silicon and determined their second-order temperature coefficients. The calculations demonstrated distinct doping-induced variations, showing pronounced decreases in C₁₁ and C₄₄ and a moderate increase in C₁₂. Finite-element analyses using the fitted temperature coefficients indicated improved thermal stability of silicon resonators through phosphorus doping. Our study explores the integration of machine learning-based atomic-scale simulations with MEMS/NEMS design, providing practical guidance for optimal dopant selection to enhance silicon resonator thermal stability. Full article

In this work, a low-cost low-power navigation solution for autonomous underwater vehicles is introduced utilizing a Micro-Electro-Mechanical System (MEMS) inertial sensor and an ultra short baseline (USBL) system. The complete signal processing is implemented on a cheap 16-bit fixed-point arithmetic microcontroller. For data fusion and calibration, an error state Kalman filter in square root form is used, which preserves stability in case of rounding errors. To further reduce the influence of rounding errors, a stochastic rounding scheme is applied. The USBL measurements are integrated using tightly coupled data fusion by deriving the observation functions separately for range, elevation, and azimuth angles. The effectiveness of the fixed point implementation with stochastic rounding is demonstrated on a simulation, and the the complete setup is tested in a field test. The results of the field test show an improved accuracy of the tightly coupled data fusion in comparison with loosely coupled data fusion. It is also shown that the applied rounding schemes can bring the fixed-point estimates to a near floating point accuracy. Full article

High-temperature micro-electro-mechanical systems (MEMSs) are critical for applications in extreme environments and applications where the operating temperature can exceed 1000 °C, but their long-term performance is limited by complex failure mechanisms, including material degradation caused by atomic migration. This study introduces a simulation-assisted approach to analyze and predict the dominant failure modes, focusing on vacancy fluxes and their driving forces, within high-temperature MEMS structures. The focus is on tungsten-based structures operating at a temperature of 1580 °C. This approach couples electric-, stress- and temperature-dependent simulations to evaluate atomic migration pathways, which are key contributors to failure. This study demonstrates that void accumulation, driven by vacancy migration, results in localized current density increase, hotspot formation, and accelerated structural degradation. The mean time to failure (MTTF) is shown to have exponential dependence on temperature and inverse-square dependence on current density, highlighting the critical role of these parameters in device reliability. These findings provide a deeper understanding of the failure mechanisms in high-temperature MEMSs and underscore the need for design strategies that mitigate electromigration and stress-induced void growth to enhance device performance and longevity. Full article

Monitoring the vibration response of Portland cement concrete (PCC) pavement under dynamic vehicle loading is critical for road maintenance and traffic analysis. This study embedded micro-electro-mechanical systems (MEMS) accelerometer sensors in PCC pavement to capture vibration signals induced by vehicles. A thresholding method is proposed to automate vehicle detection by analyzing acceleration time-domain data, achieving precision and recall rates exceeding 85%. The study also explored various sensor placement locations and different threshold values for acceleration time-domain signals. Sensor placement optimization revealed that positioning sensors at the front or rear ends of pavement slabs maximizes vibration response, enabling low-cost and efficient detection. Experimental results demonstrated that the proposed method balances simplicity and accuracy, eliminating the need for complex denoising processes. This approach provides a cost-effective solution for real-time vehicle detection and enhances pavement performance monitoring, supporting improved maintenance and traffic management strategies. Full article

Lakes are complex ecosystems affected by various anthropogenic influences, including vessel-induced waves. Detecting these waves by a micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU) equipped on a buoy-based monitoring system can help assess their impacts and support developing sustainable water ecosystem management. This study evaluated and optimized the measurement accuracy of a wave-monitoring system designed to detect waves generated by recreational vessels on lakes. In laboratory tests, we analyzed and, separately, compared the hydraulic behavior of different buoy configurations and assessed the IMU integration in field test campaigns. Results showed that all tested buoys exhibited a mean average absolute deviation (AAD) of less than 20 mm, while the IMU integration achieved an overall AAD of $1.9$ mm. For small waves, characterized by wave heights < 50 mm, the IMU’s AAD corresponds to the buoy’s AAD. However, for larger waves, the buoy’s AAD often significantly exceeds that of the IMU, indicating that the hydraulic performance of the buoy limits measurement accuracy in case of greater waves. The best-performing buoy configuration in laboratory tests achieved a measurement accuracy (mean AAD) below 10 mm (or $10\%$ of wave height), confirming the suitability of the developed wave buoys for a vessel-induced wave-monitoring system on lakes. Full article