Search Results (135)

This article reviews the reactive magnetron sputtering of piezoelectric Aluminium Nitride (AlN) thin films, with a focus on process parameter optimisation and system-level deposition challenges. AlN is a leading material for MEMS and RF applications owing to its c-axis (002) orientation, high acoustic velocity, wide bandgap (∼6.2 eV), and CMOS compatibility. We review the influence of sputtering power, nitrogen flow ratio, substrate temperature, and target-to-substrate distance on crystallographic quality and document practical hardware challenges, including vacuum leakage, grounding faults, target erosion, and mass flow controller drift, that critically affect reproducibility but are systematically underreported in the literature. A perspective is provided on emerging application domains where optimised AlN films address current performance gaps, including next-generation RF/telecom systems towards 6G and Future Networks, harsh environment sensing and actuation, biomedical ultrasound, and IoT energy harvesting. The complementarity between AlN and Silicon Carbide (SiC) is discussed for high-temperature, high-power, and radiation-hard MEMS, where AlN/SiC heterostructures combine the piezoelectric activity of AlN with the mechanical and chemical robustness of SiC. It also incorporates a discussion of dopant- and heteroepitaxy-based AlN engineering, AlN deposition on a wider range of substrates, the role of seed and electrode underlayers, and pulsed-DC sputtering as a third power supply mode alongside RF and conventional DC. Full article

Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold t_c. This paper presents BAZ II, a simulation-validated multi-agent navigation system that extends the analytical BAZ (bifurcation-aware zonal navigation) framework. Its central idea is to treat communication quality as a planning resource and combine it with multi-agent collaboration, making the navigation cliff a manageable degradation event rather than a hard operating limit. Four contributions support this idea: a thermalhysteresis MEMS gyroscope drift model reproduces the analytical cliff in simulation and supplies its physical mechanism; a distributed collaborative simultaneous localization and mapping (SLAM) filter coupled to a stochastic continuous-time Markov chain (CTMC) interagent channel sustains GPS-denied localization within the operational accuracy budget; a 3D Gaussian process RF-aware model predictive controller (MPC) with cognitive radio frequency-hopping restores link availability under jamming, while an analytic hierarchy process (AHP)-weighted multi-objective communication cost improves latency and jitter at negligible signal-to-noise ratio cost; finally, the integrated controller executes within the onboard real-time budget of an NVIDIA Jetson Xavier NX. All results are obtained in simulation, with hardware-in-the-loop and field testing remaining as priority future work. Full article

Aggressive technology scaling has led to a significant increase in manufacturing process variations and transistor aging effects, which critically degrade the performance of radio frequency (RF) circuits. These reliability challenges are particularly pronounced in voltage-controlled oscillators (VCOs), where phase noise and operating frequency stability are compromised. While design strategies incorporating micro-electromechanical systems (MEMS) actuators enhance VCO performance by leveraging MEMS varactors or inductors with substantially higher quality factors (Q), this benefit is progressively undermined over time by process variations and aging-induced shifts in the threshold voltage and carrier mobility of the VCO’s transistors. This work presents an ultra-low-power current-reuse voltage-controlled oscillator (VCO) designed to maintain stable performance under process variability and reliability-induced parameter shifts. Robust operation is achieved using a self-detecting–correcting (SDC) bias scheme that senses performance drift and applies corrective feedback through body-bias control in the VCO core. Analytical relations are derived to describe the impact of threshold voltage and mobility variations, and the approach is validated via post-layout simulations in a 130 nm complementary metal-oxide semiconductor (CMOS). Under 18% variations in threshold voltage and carrier mobility, the proposed SDC scheme preserves oscillation frequency, phase noise, and figure of merit (FoM) while also mitigating the intrinsic output amplitude imbalance of conventional current-reuse VCOs. Monte Carlo analysis (500 runs) demonstrates low sensitivity to fabrication uncertainty, with a standard deviation below 0.14 dBc/Hz for phase noise, 210 kHz for oscillation frequency, and 0.4 dBc/Hz for FoM. The VCO operates from a 0.9 V supply, consumes 175 μW, and achieves −124 dBc/Hz phase noise at 1 MHz offset near 2.4 GHz (FoM ≈ −199 dBc/Hz). Full article

Integration of lead zirconate titanate (PZT) films on metallic substrates is important for flexible piezoelectric devices, but achieving highly textured crystallinity without detrimental interfacial diffusion or oxidation remains challenging. In this work, PZT thick films (~1.3 μm) were deposited on titanium substrates using radio-frequency magnetron sputtering at 400 °C followed by rapid thermal processing at 640 °C for 2.5 min. A conductive LaNiO₃ buffer layer was introduced to promote the nucleation of the perovskite phase and suppress interfacial degradation. The resulting PZT films on the LNO/Pt/Ti substrates exhibit a strong (001) preferred orientation and a dense microstructure. The films show a large remnant polarization P_r of ~61 μC cm⁻² and a low coercive field E_c of ~56 kV cm⁻¹ at 60 V, together with a dielectric constant ε_r of ~1350–1612 and a dielectric loss tanδ ≤ 0.06 in the frequency range of 1 kHz to 1 MHz. Patterned Pt/PZT/LNO/Pt/Ti cantilevers yield a transverse piezoelectric coefficient e_31,f of ~−6.7 C/m², significantly outperforming reported piezoelectric films deposited on Ti. These results demonstrate that controlled nucleation and rapid thermal crystallization enable highly textured PZT films on reactive metallic substrates, providing a viable route for flexible piezoelectric MEMS devices. Full article

In light of sustainable development and the growing global emphasis on implementing green practices across all scientific sectors, the present work introduces a comprehensive framework with contemporary sustainability imperatives in nanofabrication cleanrooms. Recognizing the unique characteristics of cleanroom-based environments, which demand controlled conditions and strict procedures, the aim of the current work is to evaluate diverse nanofabrication scenarios with regard to their sustainability perspective with focus on operations at research- and pilot-line-level facilities. Full article

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article

Porous silicon (PSi), characterized by its high specific surface area and highly tunable morphology, presents significant potential across optoelectronics, energy storage, and biomedical applications. This review provides a systematic analysis of the synthesis methodologies, interfacial chemical engineering, and diverse applications of PSi. Initially, fabrication techniques are examined, contrasting the pore formation mechanisms of electrochemical anodization, metal-assisted chemical etching (MACE), and emerging vapor-phase etching methods, while elucidating the control of geometric parameters from microporous to macroporous scales. To address the thermodynamic instability of the hydride-terminated surface, this review systematically evaluates modification strategies such as thermal oxidation, hydrosilylation, carbonization, and atomic layer deposition (ALD). We critically analyze their efficacy in mitigating oxidative drift and enabling specific functionalization. Subsequently, the review summarizes current applications in sensing (refractive index and photoluminescence modulation), energy storage (lithium-ion battery anodes and supercapacitors), and microsystem technologies (radio frequency (RF) isolation, gettering, and micro-electro-mechanical systems (MEMS) sacrificial layers), emphasizing the critical role of structure–property relationships. Finally, an objective assessment is provided regarding the challenges in translating PSi technology to industrial scales, specifically addressing the trade-offs between biodegradability and stability, wafer-scale process uniformity, and the compatibility of wet-chemical processing with standard complementary metal–oxide–semiconductor (CMOS) integration flows. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Accurate identification of liquor vintage is crucial for ensuring product authenticity and optimizing market value, as the price and sensory quality of liquor increase with age. Traditional sensory evaluation by sommeliers is inherently limited by subjectivity, physiological fatigue, and inconsistency, posing challenges for reliable large-scale quality assessment. To address these limitations, this study introduces an innovative homemade electronic tongue (ET) system integrated with machine learning and deep learning algorithms for rapid and precise vintage identification. The ET system, consisting of six metallic electrodes and a MEMS-based temperature sensor, successfully discriminated five consecutive liquor vintages produced at one-year intervals. Using Support Vector Machine (SVM) and Random Forest (RF) algorithms, classification accuracies of 91.0% and 78.0% were achieved, respectively. Remarkably, the proposed one-dimensional convolutional neural network (1D-CNN) model further improved the recognition accuracy to 94.0%, representing the highest reported performance for ET-based vintage prediction to date. The findings demonstrate that the integration of multi-electrode electrochemical sensing with artificial intelligence enables objective, reproducible, and high-throughput evaluation of liquor aging characteristics. This approach provides a scientifically robust alternative to human sensory analysis, offering significant potential for counterfeit detection, liquor authentication, and the broader assessment of food and beverage quality within molecular sensing frameworks. Full article

The growing demand for wideband electronically scanned arrays (ESAs) in next-generation radar, satellite, and 5G/6G systems has renewed interest in true time delay units (TDUs) to overcome the limitations of phase-based beamforming. In parallel, recent advances in the commercial availability and reliability of packaged RF MEMS switches have enabled practical hardware implementations once considered infeasible. This paper presents the design, fabrication, and experimental validation of a broadband, 4-bit switched-line TDU using only off-the-shelf components and standard PCB processes. The unit operates from 0.4 to 6 GHz, with a total delay range of 0–413 ps, achieving an average insertion loss of 1.5 dB and delay error below 18.4 ps, resulting in a figure of merit (FOM) of 152.8 ps/dB. Measured results are reported alongside a refined switch/termination model that aligns simulations with measurements. This is among the first reported demonstrations of a complete RF MEMS-based TDU implemented entirely with commercially available components in a standard PCB-integrated implementation. These results demonstrate a practical pathway toward scalable MEMS-based TDUs for deployment in advanced beamforming systems. Full article

This paper provides a comprehensive review of recent advancements in radio-frequency (RF) multifunctional components with integrated filtering characteristics, including tunable filtering attenuators, filtering power dividers, filtering couplers, and filtering Butler matrices, all of which play critical roles in wireless communication systems. With the increasing demand for miniaturization, integration, and low-loss performance in RF front-ends, multifunctional components with filtering characteristics have become essential. This review first introduces tunable attenuators and filtering attenuators based on various technologies such as PIN diodes, graphene-based structures, and RF-MEMS switches, and also analyzes their advantages, limitations, and performance. Then, we discuss filtering power dividers developed from Wilkinson structures, three-line coupled structures, resonator-based coupling matrix methods, and SSPP-waveguide hybrids. Furthermore, filtering couplers and filtering Butler matrices are reviewed, highlighting their capability to simultaneously achieve amplitude and phase control, making them suitable for multi-beam antenna feeding networks. Finally, a brief conclusion is summarized. Future research directions, such as hybrid technologies, novel materials, broadband and multi-band designs, and antenna-matrix co-design, are suggested to further enhance the performance and practicality of multifunctional RF components for next-generation wireless communication systems. Full article

As a core functional device in microwave systems, attenuators play a crucial role in key aspects such as signal power regulation, amplitude attenuation, and impedance matching. Addressing the pressing technical issues currently exposed by attenuators in practical applications, such as excessive insertion loss, low attenuation accuracy, large physical dimensions, and insufficient process reliability, this paper proposes a design scheme for an RF three-bit reconfigurable stepped attenuator based on radio frequency micro-electromechanical systems (RF MEMS) switches. The attenuator employs planar integration of the T-type attenuation network, Coplanar Waveguide (CPW), Y-shaped power divider, and RF MEMS switches. While ensuring rational power distribution and stable attenuation performance over the full bandwidth, it reduces the number of switches to suppress parasitic parameters, thereby enhancing process feasibility. Test results confirm that this device demonstrates significant advancements in attenuation accuracy, achieving a precision of 1.18 dB across the 0–25 dB operational range from DC to 20 GHz, with insertion loss kept below 1.65 dB and return loss exceeding 12.15 dB. Additionally, the device boasts a compact size of merely 0.66 mm × 1.38 mm × 0.32 mm, significantly smaller than analogous products documented in existing literature. Meanwhile, its service life approaches 5 × 10⁷ cycles. Together, these two attributes validate the device’s performance reliability and miniaturization advantages. Full article

The rapid growth of low-power Internet of Things (IoT) applications has created an urgent demand for compact, battery-free power solutions. However, most existing RF energy harvesters rely on active rectifiers, multi-phase topologies, or complex tuning networks, which increase circuit complexity and static power overhead while struggling to maintain high efficiency under microwatt-level inputs. To address this challenge, this work proposes a harmonic-recycling, passive, RF-energy-harvesting system with integrated power management (HR-P-RFEH). The system adopts a planar microstrip architecture compatible with MEMS fabrication, integrating a dual-stage voltage multiplier rectifier (VMR) and a stub-based harmonic suppression–recycling network. The design was verified through combined electromagnetic/circuit co-simulations, PCB prototyping, and experimental measurements. Operating at 915 MHz under a 0 dBm input and a 2 k$\Omega$ load, the HR-P-RFEH achieves a stable 1.4 V DC output and a peak rectification efficiency of 70.7%. Compared with a conventional single-stage rectifier, it improves the output voltage by 22.5% and the efficiency by 16.4%. The rectified power is further regulated by a BQ25570-based unit to provide a stable 3.3 V supply buffered by a 47 mF supercapacitor, ensuring continuous operation under intermittent RF input. In comparison with the state of the art, the proposed fully passive, harmonic-recycling design achieves competitive efficiency without active bias or adaptive tuning while remaining MEMS- and LTCC-ready. These results highlight HR-P-RFEH as a scalable and fabrication-friendly building block for next-generation energy-autonomous IoT and MEMS systems. Full article

This research investigates resonator width optimization for simultaneously enhancing electrical performance and mechanical reliability in wideband RF MEMS filters through systematic evaluation of three configurations: 0% (L1), 60% (L2), and 100% (L3) matching ratios between cap and bottom wafers using Au-Au thermocompression bonding. The study demonstrates that resonator width alignment significantly influences both electromagnetic field coupling and bonding interface integrity. The L3 configuration with complete width matching achieved optimal RF performance, demonstrating 3.34 dB insertion loss across 4.5 GHz bandwidth (25% fractional bandwidth), outperforming L2 (3.56 dB) and L1 (3.10 dB), while providing enhanced electromagnetic wave coupling and minimized contact resistance. Mechanical reliability testing revealed superior bonding strength for the L3 configuration, withstanding up to 7.14 Kgf in shear pull tests, significantly exceeding L1 (4.22 Kgf) and L2 (2.24 Kgf). SEM analysis confirmed uniform bonding interfaces with minimal void formation (~180 nm), while Q-factor measurements showed L3 achieved optimal loaded Q-factor (Q_L = 3.31) suitable for wideband operation. Comprehensive environmental testing, including thermal cycling (−50 °C to +145 °C) and humidity exposure per MIL-STD-810E standards, validated long-term stability across all configurations. This investigation establishes that complete resonator width matching between cap and bottom wafers optimizes both electromagnetic performance and mechanical bonding reliability, providing a validated framework for developing high-performance, reliable RF MEMS devices for next-generation communication, radar, and sensing applications. Full article