Search Results (878)

Capacitive micromachined ultrasonic transducers (CMUTs) have been widely applied in fields such as air-coupled ultrasonic nondestructive testing, gesture recognition, and 3D imaging. However, most current CMUTs struggle to simultaneously achieve both low power consumption and high performance, which limits their application in relevant fields. In this paper, a dual-layer CMUT is proposed, and its structural optimization design is also analyzed. The dual-layer CMUT consists of a top-layer circular CMUT cell and a bottom-layer annular CMUT cell. A movable pillar connects the top and bottom cells of the double-layer CMUT. This design increases the total deflection and reduces the stiffness, making the membrane more susceptible to deformation under external forces, thereby achieving low power consumption and high reception performance. The finite element method (FEM) results showed that, compared with conventional CMUTs, the structural optimization design of the dual-layer CMUT had a 13.7% reduction in collapse voltage. The improvements in the maximum deflection, average deflection, electromechanical coupling coefficient, transmitting sensitivity, and receiving sensitivity were 41.2%, 68.0%, 84.6%, 17.7%, and 101.6%, respectively. Therefore, the dual-layer CMUT has low power consumption and high reception performance while maintaining transmission performance, and it has potential for applications in portable, low-power devices and air-coupled ultrasonic nondestructive testing. Full article

This study presents a comprehensive multi-objective optimization framework specifically designed for micro-electromechanical systems (MEMS). The framework integrates both traditional and adaptive optimization techniques, named Surrogate-Assisted Multi-Objective Optimization (SAMOO) and Adaptive-SAMOO (A-SAMOO), respectively. By addressing key limitations of traditional approaches, such as the consideration of objective constraints and the provision of multiple design options, the proposed framework enhances both flexibility and practical applicability. Results show that adaptive optimization outperforms traditional offline methods by delivering a greater number and higher quality of optimal solutions while requiring fewer finite element method simulations. The adaptive approach showed a significant advantage by attaining high-quality solutions while requiring only 2.8% of the finite element method (FEM) evaluations compared to traditional methods that do not incorporate surrogate models. This performance boost highlights the advantages of online learning in enhancing the accuracy, speed, and diversity of solutions in MEMS optimization. These optimization schemes were tested on multiple MEMS devices with varying physics and complexities, specifically the U-shaped Lorentz force actuator, serpentine Lorentz force actuator, and thermal actuator. The results highlight the robustness and versatility of the proposed methods, particularly in addressing cases involving discrete design variables and strict objective constraints. This comprehensive, step-by-step framework serves as a valuable resource for researchers and practitioners aiming to optimize MEMS designs from the ground up, providing a reliable and effective approach to multi-objective optimization in MEMS applications. Full article

Designing and manufacturing devices at the micro- and nanoscales offers significant advantages, including high precision, quick response times, high energy density ratios, and low production costs. These benefits have driven extensive research in micro-electromechanical systems (MEMS) and nano-electromechanical systems (NEMS), resulting in various classifications of materials and manufacturing techniques, which are ultimately used to produce different classifications of MEMS devices. The current work aims to systematically organize the literature on MEMS in biomedical devices, encompassing past achievements, present developments, and future prospects. This paper reviews the current research trends, highlighting significant material advancements and emerging technologies in biomedical MEMS in order to meet the current challenges facing the field, such as ensuring biocompatibility, achieving miniaturization, and maintaining precise control in biological environments. It also explores projected applications, including use in advanced diagnostic tools, targeted drug delivery systems, and innovative therapeutic devices. By mapping out these trends and prospects, this review will help identify current research gaps in the biomedical MEMS field. By pinpointing these gaps, researchers can focus on addressing unmet needs and advancing state-of-the-art biomedical MEMS technology. Ultimately, this can lead to the development of more effective and innovative biomedical devices, improving patient care and outcomes. Full article

While extensive research has addressed electromechanical systems interacting with power electronic converters, most studies lack a unified modeling framework that simultaneously captures converter switching behavior, nonlinear dynamics, and linearized control-oriented representations. In particular, the dynamic interaction between two-level full-bridge converters and angular-motion electromechanical switching devices (EMDs) is often simplified or abstracted, thereby limiting control system design and frequency-domain analysis. This work presents a comprehensive dynamic modeling methodology for an angular-motion EMD driven by a full-bridge dc-dc converter. The modeling framework includes (i) a detailed nonlinear switching model, (ii) an averaged nonlinear model suitable for control design, and (iii) a small-signal linearized model for deriving transfer functions and evaluating system stability. The proposed models are rigorously validated through time-domain simulations and Bode frequency analysis, confirming both theoretical equilibrium points and dynamic characteristics such as resonant frequencies and phase margins. The results demonstrate strong consistency across the modeling hierarchy and reveal critical features—such as ripple-induced resonance and low-frequency coupling—that are essential for robust controller design. This framework established a foundational tool for advancing the control and optimization of electromechanical switching systems in high-performance applications. Full article

A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as inspection, cleaning, maintenance, and rescue while maintaining stable adhesion to the surface. Its applications span various sectors, including industrial maintenance, marine engineering, and aerospace manufacturing. This paper provides a systematic review of the physical principles and scalability of various attachment methods used in wall-climbing robots, with a focus on the applicability and limitations of different attachment mechanisms in relation to robot size and structural design. For specific attachment methods, the design and compatibility of motion and attachment mechanisms are analyzed to offer design guidance for wall-climbing robots tailored to different operational tasks. Additionally, this paper reviews localization and path planning methods for wall-climbing robots, comparing graph search, sampling-based, and feedback-based algorithms to guide strategy selection across varying environments and tasks. Finally, this paper outlines future development trends in wall-climbing robots, including the diversification of locomotion mechanisms, hybridization of attachment systems, and advancements in intelligent localization and path planning. This work provides a comprehensive theoretical foundation and practical reference for the design and application of wall-climbing robots. Full article

This study examined the relationship between front crawl trunk incline and the lower limbs’ biomechanics in non-expert swimmers. Eighteen male participants (19.22 ± 1.11 years) were recorded in the sagittal plane performing 2 × 25 m of front crawl at maximum intensity to analyze their trunk incline (TI), maximum knee angle (KneeMax), minimum knee angle (KneeMin), knee range of motion (KneeROM), kicking duration (KickDur), descendent phase duration (DurDesc), and ascendant phase duration (DurAsc). They also performed towing for passive drag measurements and a 20 s lower limbs’ tethered test while connected to an electromechanical device and grabbing a floating board to collect the maximum (Fmax) and mean (Fmean) kicking forces. Pearson’s correlation coefficient (r) was used to compute the relationships between all variables. For kinematics, a negative association was found between the TI and v (r = −0.64), KneeMin (r = −0.68), KneeRoM (r = −0.74), and SI (r = −0.52). Regarding kinetics, a single association was found between TI and Fmean (r = −0.52). The results indicate that a greater TI in non-expert swimmers may be a consequence of weaker knee action, which compromises their mean force application and negatively affects velocity and efficiency. Full article

Tunable photonic devices are increasingly pivotal in modern optical systems, enabling the dynamic control over light propagation, modulation, and filtering. This review systematically explores two prominent classes of materials, thermo-optic and electro-optic, for their roles in such tunable devices. Thermo-optic materials utilize refractive index changes induced by temperature variations, offering simple implementation and broad material compatibility, although often at the cost of slower response times. In contrast, electro-optic materials, particularly those exhibiting the Pockels and Kerr effects, enable rapid and precise refractive index modulation under electric fields, making them suitable for high-speed applications. The paper discusses the underlying physical mechanisms, material properties, and typical figures of merit for each category, alongside recent advancements in organic, polymeric, and inorganic systems. Furthermore, integrated photonic platforms and emerging hybrid material systems are highlighted for their potential to enhance performance and scalability. By evaluating the tradeoffs in speed, power consumption, and integration complexity, this review identifies key trends and future directions for deploying thermo-optic and electro-optic materials in the next generation tunable photonic devices. 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

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

Flexoelectricity arises in materials under strain gradients, which can be particularly significant for situations in which the existence of other electromechanical properties is absent or generating large flexoelectric properties is achievable. This effect has also been observed in some biological materials, whose understanding can hugely help to further enhance our understanding of vital biological processes like mechanotransduction, as well as the development of applications in regenerative medicine and drug delivery. While the field of flexoelectricity as a relevant topic in biological materials is relatively new and still developing, the current study aims to review available results on flexoelectric effects in biological materials such as cells and cell membranes, hearing mechanisms, and bone, and their potential applications in biomedical research. Therefore, we first provide a brief background on two main electromechanical couplings (piezoelectricity and flexoelectricity) and further, how flexoelectricity has been experimentally and theoretically identified. We then review flexoelectricity in different biological materials as the main aim of the current study. Within that, we provide additional emphasis on the influence of this effect on bone and bone remodeling. In particular, the study outlines current limitations and provides potential directions for future work, emphasizing the crucial role in the development of next-generation electromechanical devices and optimizing their function in the area of biomedical research. 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

In the context of new power systems, the safe and accurate sensing of voltage data is crucial for the secure and stable operation of power grids. Given that existing voltage measurement devices cannot meet the development requirements for wide-area deployment and distributed monitoring, this paper designs a non-intrusive voltage measurement device based on MEMS (micro-electromechanical system) electric field sensors, which are characterized by their small size, low power consumption, ease of installation and strong anti-interference ability. Firstly, the paper introduces the voltage measurement principle and analyzes the equivalent circuit based on this analysis; secondly, the key structural design of the measurement device is completed and the prototype of the device is developed; finally, the accuracy and anti-jamming tests of the measurement device are conducted by establishing an experimental platform, followed by field applications. Experimental results demonstrate that the voltage measurement device has high measurement accuracy, and the maximum error is only 1.215%. Additionally, the device has a good shielding capability against the coupled electric field of surrounding interference conductors, with a maximum error increase of 1.313%. In a 10 kV overhead line voltage test, the device can accurately obtain the actual voltage. The voltage measuring device developed in this paper can provide data support for the condition assessment of overhead lines and effective monitoring means for the safe and stable operation of the power system. Full article

The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns. 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