Micromachines最新文献

In the microinjection molding process, continuous monitoring is important for optimization of the process and control. In microfluidic or lab-on-chip devices, defective microfeatures can compromise biological assays and diagnostic results, and therefore, the quality of these features is a critical issue. Microfeatures can be inspected using advanced inspection and microscopic techniques, but these are expensive, time-consuming, and difficult to use for full-scale production. We present here a new technique for quality control of microfeatures, which uses the filling of a controlled microcavity inside or outside the molded part as a quality control tool for filling microfeatures. Micro gaps (checkpoints) are used as an indicator of microfeature filling. Two micro gaps can be used for filling (checkpoints) as a Go/No-Go gauge.

We systematically investigated the optical properties of P-N nanoporous silicon (NPS) diodes fabricated using photoelectrochemical etching and ultrasonic vibration (PEEU), followed by liquid-phase bonding and thermal treatment. Ultrasonic vibration during etching promoted uniform pore formation by enhancing reactant diffusion and suppressing hydrogen bubble accumulation, while laser-induced photocarriers improved etching selectivity, facilitating the formation of NPS with pronounced quantum confinement. The fabricated NPS devices exhibited significantly enhanced photoluminescence (PL) and electroluminescence (EL) properties, with an average external quantum efficiency of 7.3% at a bias of 10 V. Subsequent liquid-phase bonding and thermal annealing further enhanced structural stability and interface quality, resulting in an 180% increase in PL intensity. These results demonstrate that the combination of PEEU with liquid-phase bonding and thermal annealing yields a versatile approach to tailor the optical and electrical properties of P-N porous silicon nanostructures for high-performance light-emitting diodes and quantum-confined silicon photonics, highlighting the critical role of process-induced nanostructures and thermal modifications in device performance.

To address the pressing need for compact and highly reliable perception systems in autonomous mobile robots, a compact bandpass filter (BPF) integrating slot-line resonator with substrate-integrated waveguide (SIW) technology for robotic millimeter-wave radar front ends was proposed. By integrating slot-line resonators between adjacent SIW cavities, the proposed design effectively increases the filtering order without increasing the layout area. This approach not only generates extra transmission poles but also creates a sharp transmission zero at the upper stopband, thereby significantly enhancing out-of-band rejection. This characteristic is crucial for robotic radar operating in complex and dynamic environments, as it effectively suppresses out-of-band interference and improves the system signal-to-noise ratio and detection reliability. To validate the performance, a prototype filter operating in the 24.25-27.5 GHz passband was fabricated. The measured results show good agreement with simulations, demonstrating low insertion loss, compact size, and wide stopband. Finally, to validate its compatibility with robotic radar modules, the chip was assembled onto a PCB using surface-mount technology. The responses of the bare die and the packaged module were then compared to evaluate the impact of integration on the overall RF performance. The proposed design offers a key filtering solution for next-generation high-performance, miniaturized robotic perception platforms.

Bond wire degradation represents the predominant failure mechanism in IGBT modules, accounting for approximately 70% of power converter failures and posing significant reliability challenges in modern power electronic systems. Existing monitoring techniques face inherent trade-offs between measurement accuracy, implementation complexity, and electromagnetic compatibility. This paper proposes a physics-constrained ensemble learning framework for non-intrusive bond wire health assessment via V_ce-on prediction. The methodological innovation lies in the synergistic integration of multidimensional feature engineering, adaptive ensemble fusion, and domain-informed regularization. A comprehensive 16-dimensional feature vector is constructed from multi-physical measurements, including electrical, thermal, and aging parameters, with novel interaction terms explicitly modeling electro-thermal stress coupling. A dynamic weighting mechanism then adaptively fuses three specialized gradient boosting models (CatBoost for high-current, LightGBM for thermal-stress, and XGBoost for late-life conditions) based on context-aware performance assessment. Finally, the meta-learner incorporates a physics-based regularization term that enforces fundamental semiconductor properties, ensuring thermodynamic consistency. Experimental validation demonstrates that the proposed framework achieves a mean absolute error of 0.0066 V and R² of 0.9998 in predicting V_ce-on, representing a 48.4% improvement over individual base models while maintaining 99.1% physical constraint compliance. These results establish a paradigm-shifting approach that harmonizes data-driven learning with physical principles, enabling accurate, robust, and practical health monitoring for next-generation power electronic systems.

A stripline-type circulator is essential for the initial low-power characterization of vacuum electron devices such as magnetrons, enabling accurate measurements of startup behavior, oscillation frequency, and mode structure while minimizing reflections and protecting diagnostic equipment. In this study, two broadband S-band stripline circulator prototypes operating in the 2-4 GHz and 3-4 GHz bands were designed, fabricated, and experimentally characterized. A unified design methodology was implemented by using the same ferrite material and coupling angle in both structures, providing procurement simplicity, cost reduction, and technological standardization. This approach also enabled a direct assessment of how bandwidth variations influence circulator behavior. The design goals targeted a transmission efficiency above 90%, isolation exceeding 15 dB, and a voltage standing-wave ratio (VSWR) of 1.2:1. Experimental evaluations, including magnetic field mapping, low-power S-parameter measurements, and high-power tests, confirmed that both prototypes satisfy these specifications, consistently achieving at least 90% transmission across their respective operating bands. Additionally, a comparative analysis between a locally fabricated ferrite and a commercial ferrite sample was conducted, revealing the influence of material properties on transmission stability and high-power behavior. The results demonstrate that broadband stripline circulators employing a common ferrite material can be adapted to different S-band applications, offering a practical, cost-effective, and reliable solution for RF systems.

Study presents a comparative analytical investigation into the green synthesis of monometallic and bimetallic nanoparticles using Punica granatum (pomegranate) extract, aimed at developing high-performance electrochemical sensors for the detection of ciprofloxacin (CIP) as a representative pharmaceutical pollutant. Three nanoparticle systems were successfully synthesized: monometallic Au@NPs and TiO₂@NPs, as well as the bimetallic AuTiO₂@NPs. Their structural and physicochemical characteristics were comprehensively analyzed using UV-Vis spectroscopy, FTIR, SEM, TEM, and XRD techniques. The obtained nanoparticles exhibited predominantly spherical morphologies with average particle sizes of approximately 40 ± 5 nm for Au@NPs, 50 ± 7 nm for TiO₂@NPs, and 60 ± 6 nm for AuTiO₂@NPs. These nanomaterials were subsequently employed to modify electrode surfaces for electrochemical sensing applications. Their analytical performance was evaluated using cyclic voltammetry (CV) and square-wave voltammetry (SWV). The sensors displayed excellent sensitivity, with limits of detection of 0.8 ppb for TiO₂@NPs, 0.8 ppb for Au@NPs, and 0.2 ppb for the AuTiO₂@NP-based sensor. The bimetallic platform demonstrated superior electrochemical behavior, enhanced signal intensity, and strong selectivity, achieving recovery rates of 98% in tap water and 103% in wastewater. Overall, the results confirm the effectiveness of green-synthesized bimetallic nanoparticles as efficient, low-cost materials for environmental monitoring of emerging pharmaceutical contaminants.

With the rapid development of semiconductor manufacturing technology, methods to effectively control the production process, reduce variation in the manufacturing process, and improve the yield rate represent important competitive factors for wafer factories. Wafer bin maps, a method for characterizing wafer defect patterns, provide valuable information for engineers to quickly identify potential root causes through accurate pattern recognition. Vision-based deep learning approaches rely on visual patterns to achieve robust performance. However, they rarely exploit the rich semantic information embedded in defect descriptions, limiting interpretability and generalization. To address this gap, we propose YOLO-LA, a lightweight prototype-based vision-language alignment framework that integrates a pretrained frozen YOLO backbone with a frozen text encoder to enhance wafer defect recognition. A learnable projection head is introduced to map visual features into a shared embedding space, enabling classification through cosine similarity Experimental results on the WM-811K dataset demonstrate that YOLO-LA consistently improves classification accuracy across different backbones while introducing minimal additional parameters. In particular, YOLOv12 achieves the fastest speed while maintaining competitive accuracy, whereas YOLOv10 benefits most from semantic prototype alignment. The proposed framework is lightweight and suitable for real-time industrial wafer inspection systems.

We present an inverse designed metalens for long-wave infrared (LWIR) imaging tailored to consumer and Internet of Things (IoT) platforms. Conventional LWIR optics either rely on costly specialty materials or suffer from low efficiency and narrow fields of view (FoV), limiting scalability. Our approach integrates adjoint-based inverse design with fabrication-aware constraints and a cone-shaped source model that efficiently captures oblique incidence during optimization. The resulting multi-level metalens achieves a wide FoV up to 100° while maintaining robust focusing efficiency and stable angle-to-position mapping on low-power 4×4 microbolometer arrays representative of edge devices. We further demonstrate application-level imaging on 4×4 microbolometer arrays, showing that the proposed metalens delivers a substantially wider FoV than a commercial narrow FoV lens while meeting low-resolution, low-cost, and low-power constraints for edge LWIR modules. By eliminating bulky multi-element stacks and reducing cost and form factor, the proposed design provides a practical pathway to compact, energy-efficient LWIR modules for consumer applications such as occupancy analytics, smart-building automation, mobile sensing, and outdoor fire surveillance.

Electrokinetics has established itself as a central pillar in microfluidic research, offering a powerful, non-mechanical means to manipulate fluids and analytes. Mechanisms such as electroosmotic flow (EOF), electrophoresis (EP), and dielectrophoresis (DEP) re-main central to the field, once more layers of complexity emerge heterogeneous interfaces, viscoelastic liquids, or anisotropic droplets are introduced. Five research directions have become prominent. Field-driven manipulation of droplets and emulsions-most strikingly Janus droplets-demonstrates how asymmetric interfacial structures generate unconventional transport modes. Electrokinetic injection techniques follow as a second focus, because sharply defined sample plugs are essential for high-resolution separations and for maintaining analytical accuracy. Control of EOF is then framed as an integrated design challenge that involves tuning surface chemistry, engineering zeta potential, implementing nanoscale patterning, and navigating non-Newtonian flow behavior. Next, electrokinetic instabilities and electrically driven micromixing are examined through the lens of vortex-mediated perturbations that break diffusion limits in low-Reynolds-number flows. Finally, electrokinetic enrichment strategies-ranging from ion concentration polarization focusing to stacking-based preconcentration-demonstrate how trace analytes can be selectively accumulated to achieve detection sensitivity. Ultimately, electrokinetics is converging towards sophisticated integrated platforms and hybrid powering schemes, promising to expand microfluidic capabilities into previously inaccessible domains for analytical chemistry and diagnostics.

The present paper investigates the steady laminar flow and thermal mixing performance of non-Newtonian Al₂O₃ nanofluids within a two-layer cross-channel micromixer, employing three-dimensional numerical simulations to solve the governing equations across a low Reynolds number range (0.1 to 50). It also addresses secondary flows and thermal mixing performance with two distinct inlet temperatures for thin nanofluids. Additionally, it explores how fluid properties and varying concentrations of Al₂O₃ nanoparticles impact thermal mixing efficiency and entropy generation. Simulations were conducted to optimize performance by adjusting the power law index (n) across different nanoparticle concentrations (1-5%). The findings show that magnetohydrodynamics can enhance mixing efficiency by generating vortices and altering flow behavior, providing important guidance for improving microfluidic system designs in practical applications.