Search Results (1,754)

With the rapid evolution of 5G wireless communications, millimeter-wave (mmWave) technology has become a crucial enabler for high-speed, low-latency, and large-scale connectivity. As the critical interface for signal transmission, mmWave antennas directly affect system performance, reliability, and application scope. This paper reviews the current state of mmWave antenna technologies in 5G systems, focusing on antenna types, design considerations, and integration strategies. We discuss how the multiple-input multiple-output (MIMO) architectures and advanced beamforming techniques enhance system capacity and link robustness. State-of-the-art integration methods, such as antenna-in-package (AiP) and chip-level integration, are examined for their importance in achieving compact and high-performance mmWave systems. Material selection and fabrication technologies—including low-loss substrates like polytetrafluoroethylene (PTFE), hydrocarbon-based materials, liquid crystal polymer (LCP), and microwave dielectric ceramics, as well as emerging processes such as low-temperature co-fired ceramics (LTCC), 3D printing, and micro-electro-mechanical systems (MEMS)—are also analyzed. Key challenges include propagation path limitations, power consumption and thermal management in highly integrated systems, cost–performance trade-offs for mass production, and interoperability standardization across vendors. Finally, we outline future research directions, including intelligent beam management, reconfigurable antennas, AI-driven designs, and hybrid mmWave–sub-6 GHz systems, highlighting the vital role of mmWave antennas in shaping next-generation wireless networks. Full article

Glass, as an amorphous material with excellent optical transparency and chemical stability, plays an irreplaceable role in modern engineering and technology fields such as semiconductor manufacturing and micro-electro-mechanical systems (MEMS). For example, borosilicate glass, with a coefficient of thermal expansion (CTE) that is close to having good thermal shock resistance and chemical stability, can be applied to MEMS packaging and aerospace fields. SiO₂ glass exhibits excellent thermal stability, extremely low optical absorption, and high light transmittance, while also possessing strong chemical stability and extremely low dielectric loss. It is widely used in semiconductors, photolithography, and micro-optical devices. However, the stress sensitivity of traditional mechanical joints and the poor weather resistance of adhesive bonding make conventional methods unsuitable for glass joining. Welding technology, with its advantages of high joint strength, structural integrity, and scalability for mass production, has emerged as a key approach for precision glass joining. In the field of glass welding, technologies such as glass brazing, ultrasonic welding, anodic bonding, and laser welding are being widely studied and applied. With the advancement of laser technology, laser welding has emerged as a key solution to overcoming the bottlenecks of conventional processes. This paper, along with the application cases for these technologies, includes an in-depth study of common issues in glass welding, such as residual stress management and interface compatibility design, as well as prospects for the future development of glass welding technology. Full article

Optical phase modulation is essential for a wide range of silicon photonic integrated circuits used in communication applications. In this study, an optical phase shifter utilizing photo-elastic effects is proposed, where mechanical stress is induced by electrostatic micro-electro-mechanical systems (MEMS) with actuators arranged in a comb drive configuration. The design incorporates suspended serpentine silicon nitride (SiN) optical waveguides. Through extensive numerical simulations, it is shown that the change in the effective refractive index (n_eff) of the optical waveguide is a function of the voltage applied to the electrostatic actuators and that such n_eff tuning can be achieved for a broad range of wavelengths. Implemented within one arm of an unbalanced Mach–Zehnder interferometer (MZI), the phase shifter achieves a phase change of π when the stressed optical path measures 4.7 mm, and the actuators are supplied with 80 V DC and consume almost no power. This results in a half-wave voltage-length product (V_πL) of 37.6 V·cm. Comparative analysis with contemporary optical phase shifters highlights the proposed design’s superior power efficiency, compact footprint, and simplified fabrication process, making it a highly efficient component for reconfigurable MEMS-based silicon nitride photonic integrated circuits. Full article

This study proposes a rigid-flexible neural optrode integrated with anti-bending SU-8 optical waveguides and locally soft peptide-functionalized microelectrodes to address the challenges of precise implantation and long-term biocompatibility in traditional neural interfaces. Fabricated via microelectromechanical systems (MEMS) technology, the optrode features a PBK/PPS/(PHE)₂ trilayer electrochemical modification that suppresses photoelectrochemical (PEC) noise by 63% and enhances charge storage capacity by 51 times. A polyethylene glycol (PEG)-enabled temporary rigid layer ensures precise implantation while allowing post-implantation restoration of flexibility and enabling positioning adjustment. In vitro tests demonstrate efficient light transmission through SU-8 waveguides in agar gel and a 63% reduction in PEC noise peaks. Biocompatibility analysis reveals that peptide-coated PI substrates improve cell viability by 32.5–37.1% compared to rigid silicon controls. In vivo validation in crucian carp midbrain successfully records local field potential (LFP) signals (60–80 μV), thereby confirming the optrode’s sensitivity and stability. This design provides a low-damage and high-resolution tool for neural circuit analysis. It also lays a technical foundation for future applications in monitoring neuronal activity and researching neurodegenerative diseases with high spatiotemporal resolution. Full article

Miniaturized pressure sensors fabricated via micro-electro-mechanical systems (MEMSs) technology are ubiquitous in modern applications. However, the massively produced MEMS pressure sensors, prior to being practically used, need to be calibrated one by one to eliminate or minimize nonlinearity and zero drift. This paper presents a systematic design for the testing and calibration process of MEMS-based absolute pressure sensors. Firstly, a numerical analysis is carried out using finite element method (FEM) simulation, which verifies the accuracy of the temperature control of the physical calibration system. The simulation results reveal a slight non-uniformity of temperature distribution, which is then taken into consideration in the calibration algorithm. Secondly, deploying a home-made calibration system, the MEMS pressure sensors are tested automatically and rapidly. The experimental results show that each batch, which consists of nine sensors, can be calibrated in 80 min. The linearity and temperature coefficient (TC) of the pressure sensors are reduced from 46.5% full-scale (FS) and −1.35 × 10⁻⁴ V·K⁻¹ to 1.5% FS and −8.8 × 10⁻⁷ V·K⁻¹. Full article

Background: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the urgent need for early, non-invasive, and accessible diagnostic tools. This study aimed to evaluate the effectiveness of a microelectromechanical systems (MEMS)-based electronic nose (E-nose) in combination with gas chromatography–mass spectrometry (GC-MS) for CRC detection through sweat volatile organic compounds (VOCs). Methods: A total of 136 sweat samples were collected from 68 volunteer participants. Samples were processed using solid-phase microextraction (SPME) and analyzed by GC-MS, while a custom-designed E-nose system comprising 14 gas sensors captured real-time VOC profiles. Data were analyzed using multivariate statistical techniques, including PCA and PLS-DA, and classified with machine learning algorithms (LDA, LR, SVM, k-NN). Results: GC-MS analysis revealed statistically significant differences between CRC patients and healthy controls (COs). Cross-validation showed that the highest classification accuracy for GC-MS data was 81% with the k-NN classifier, whereas E-nose data achieved up to 97% accuracy using the LDA classifier. Conclusions: Sweat volatilome analysis, supported by advanced data processing and complementary use of E-nose technology and GC-MS, demonstrates strong potential as a reliable, non-invasive approach for early CRC detection. Full article

Lidar has been extensively used in various applications, such as autonomous driving, robot navigation, and drone obstacle avoidance, due to its advantages of a high resolution, high-ranging accuracy, and strong anti-interference ability. The micro-electro-mechanical systems (MEMS) lidar technology approach has gained popularity due to its miniaturization and semi-solid state. However, the small scanning angle of the MEMS scanning micromirror and the associated radar system cause issues, such as a limited scanning range and low spatial resolution, which hinder the wider use of MEMS lidar. To address the problems caused by the small scanning angle of the MEMS micromirror and the limitations of the current optical system, this study suggests a new MEMS lidar transmitting optical system that offers a wide scanning angle and high spatial resolution. It is based on an array reflector group and a Fresnel lens, which enables the large-angle scanning of the target area while maintaining high spatial resolution. The scanning range is 120° × 60°, the spatial resolution is 0.05° × 0.25°, and the beam-filling ratio reaches 90.63%. Full article

A 16-channel dual-sided flexible electrode based on a polyimide substrate was designed and fabricated using micro-electromechanical system (MEMS) technology. The electrode exhibited an average impedance of 5.9 kΩ at 1 kHz and a charge storage capacity (CSC) of 10.63 mC/cm². Concurrently, a three-dimensional finite element model incorporating electrical stimulation and micromotion-induced damage was established. The simulation results demonstrated that the implantation trauma caused by the bilateral electrode was significantly lower compared with silicon-based and cylindrical electrodes, while also enabling directional stimulation. Furthermore, leveraging the design of experiments (DOE) methodology, a multivariate regression model was developed to investigate the influence of key stimulation parameters—namely, current amplitude, frequency, and pulse width—on the volume of tissue activated (VTA). The results indicated that the regression model provided accurate predictions of VTA (R² = 0.912). Among the parameters, current amplitude and pulse width exerted a statistically significant influence on VTA size (p < 0.001), whereas the effect of frequency was comparatively minor (p = 0.387 > 0.05). This study presents the first successful fabrication and comprehensive dose–response analysis of a flexible bilateral DBS electrode. Its attributes of low implantation trauma, multi-channel capability, and directional stimulation offer a novel paradigm for precise neuromodulation. Additionally, the established stimulation parameter–VTA response model provides a robust theoretical foundation for optimizing therapeutic parameters in subsequent clinical applications. Full article

This study presents the design, implementation, and experimental validation of a self-contained earthquake early warning system (EEWS) based on real-time frequency-domain analysis of ground motion. The proposed system integrates a low-noise triaxial micro-electro-mechanical system (MEMS) accelerometer with a high-performance microcontroller, enabling autonomous seismic event detection without dependence on external communications or centralized infrastructure. The characteristic period of ground motion (τ_c) is estimated using a spectral moment method applied to the first three seconds of vertical acceleration following P-wave arrival. Event triggering is based on a short-term average/long-term average (STA/LTA) algorithm, with alarm logic incorporating both spectral and amplitude thresholds to reduce false positives from low-intensity or distant events. Experimental validation was conducted using a custom-built uniaxial shaking table, replaying 10 real earthquake records (Mw 4.1–7.7) in 20 repeated trials each. Results show high repeatability in τ_c estimation and strong correlation with event magnitude, demonstrating the system’s reliability. The findings confirm that modern embedded platforms can deliver rapid, robust, and cost-effective seismic warning capabilities. The proposed EEW solution is well-suited for deployment in critical infrastructure and resource-limited seismic regions, supporting scalable and decentralized early warning applications. Full article

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions. Full article

Vehicle load is crucial for road design, maintenance, and expansion, while vehicle speed and lateral position are essential for traffic management and driving safety. This paper introduces a method for collecting vehicle speed, lateral position, and load information using roadside Micro-Electromechanical Systems (MEMS) accelerometers located on the pavement. Firstly, this research analyzes the distribution of pavement vibration responses in both lateral and vertical directions based on the Finite Element Method (FEM) data provided in the literature. Then, pavement vibration data is collected by roadside sensors with a Full-scale Accelerated Loading Tester, considering varying vehicle speeds, loads, and lateral positions. The results reveal that the vertical peak acceleration increases linearly with vehicle speed within a range of 5–22 km/h, decreases following a power law as the lateral distance between the wheel center and sensor increases from 0.4 to 0.9 m, which is consistent with the trends observed in the literature’s FEM data. The vibration energy of the vertical acceleration exhibits a positive linear correlation with the total vehicle load, with a correlation coefficient of 0.885. This approach offers a practical method for vehicle load estimation, optimal sensor deployment, and enhancement of pavement performance monitoring systems. Full article

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

Microelectromechanical system (MEMS) devices are specialized electronic devices that integrate the benefits of both mechanical and electrical structures. However, the contact behavior between the interfaces of these structures can significantly impact the performance of MEMS devices, particularly when the surface roughness approaches the characteristic size of the devices. In such cases, the contact between the interfaces is not a perfect face-to-face interaction but occurs through point-to-point contact. As a result, the contact area changes with varying contact pressures and surface roughness, influencing the thermal and electrical performance. By integrating the CMY model with finite element simulations, we systematically explored the thermal conductance regulation mechanism of MEMS contact switches. We analyzed the effects of the contact pressure, micro-hardness, surface roughness, and other parameters on thermal conductance, providing essential theoretical support for enhancing reliability and optimizing thermal management in MEMS contact switches. We examined the thermal contact, gap, and joint conductance of an MEMS switch under different contact pressures, micro-hardness values, and surface roughness levels using the CMY model. Our findings show that both the thermal contact and gap conductance increase with higher contact pressure. For a fixed contact pressure, the thermal contact conductance decreases with rising micro-hardness and root mean square (RMS) surface roughness but increases with a higher mean asperity slope. Notably, the thermal gap conductance is considerably lower than the thermal contact conductance. Full article

The stable operation of micromachine systems relies on reliable power management, where DC-DC converters provide energy with high efficiency to extend operational endurance. However, these converters also constitute significant electromagnetic interference (EMI) sources that may interfere with the normal functioning of micro-electromechanical systems. This paper proposes a boost converter utilizing Pulse Width Modulation (PWM) with peak current mode control to address the EMI issues inherent in the switching operation of DC-DC converters. The converter incorporates a Hybrid Spread Spectrum (HSS) technique to effectively mitigate EMI noise. The HSS combines a 1.2 MHz pseudo-random spread spectrum with a 9.4 kHz triangular periodic spread spectrum. At a standard switching frequency of 2 MHz, the spread spectrum range is set to ±7.8%. Simulations conducted using a 0.5 μm Bipolar Complementary Metal-Oxide-Semiconductor Double-diffused Metal-Oxide-Semiconductor (BCD) process demonstrate that the HSS technique reduces EMI around the switching frequency by 12.29 dBμV, while the converter’s efficiency decreases by less than 1%. Full article

This article aims to utilize a microelectromechanical system (MEMS) to modulate coupling behavior of silicon nitride (Si₃N₄) waveguides to perform an optical switch based on a directional coupling (DC) mechanism. There are two states of the switch. First state, a Si₃N₄ wire is initially positioned up suspended in the air. In the second state, this wire will be moved down to be placed between two arms of the DC waveguides, changing the coupling behavior to achieve bar and cross states of the optical switch function. In the future, the MEMS will be used to move this wire down. In this work, we present simulations of the two static states to optimize the DC structure parameters. Based on the simulated results, the device size is 8.8 μm × 55 μm. The insertion loss is calculated to be approximately 0.24 dB and 0.33 dB, the extinction ratio is approximately 24.70 dB and 25.46 dB, and the crosstalk is approximately −24.60 dB and −25.56 dB, respectively. In the C band of optical communication, the insertion loss ranges from 0.18 dB to 0.47 dB. As such, this device will exhibit excellent optical switch performance and provide advantages in many integrated optics-related optical systems applications. Furthermore, it can be used in optical communications, data centers, LiDAR, and so on, enhancing important reference value for such applications. Full article