Search Results (1,069)

Considering the rapid iteration of you-only-look-once (YOLO)-series algorithms, this paper aims to provide a data-driven performance spectrum and selection guide for the latest YOLO series algorithm (YOLOv8 to YOLOv13) in printed circuit board (PCB) automatic optical inspection (AOI) through systematic benchmarking. A comprehensive evaluation of the six state-of-the-art YOLO series algorithms is conducted on a standardized dataset containing six typical PCB defects: missing hole, mouse bite, open circuit, short circuit, spur, and spurious copper. An innovative dual-cycle comparative experiment (100 rounds and 500 rounds) is designed, and a systematic assessment is performed across multiple dimensions, including accuracy, efficiency, and inference speed. The experimental results have revealed significant variations in algorithm performance with training cycles: under short-term training (100 rounds), YOLOv13 achieves leading detection performance (mAP50 = 0.924, mAP50-95 = 0.484) with the fewest parameters (2.45 million); after full training (500 rounds), YOLOv10 achieves the highest overall accuracy (mAP50 = 0.946, mAP50-95 = 0.526); additionally, YOLOv11 shows the optimal speed-accuracy balance after long-term training, while YOLOv12 excels in short-term training; moreover, “open circuit” and “spur” are evaluated as the most challenging defect categories to detect. The findings given in this paper indicate the absence of a universally applicable “all-in-one” algorithm and propose a clear algorithm selection roadmap: YOLOv10 is recommended for offline analysis scenarios prioritizing extreme accuracy; YOLOv13 is the top choice for applications requiring rapid iteration with tight training time constraints; and YOLOv11 is the best option for high-throughput online inspection PCB production lines. Full article

This article explores the high-frequency characteristics of gallium nitride (GaN) power-switching devices and evaluates their application performance using a double-pulse test (DPT) circuit model. With the increasing adoption of GaN power-switching devices in high-performance and miniaturized electronic products, their low junction capacitance makes them highly suitable for high-frequency applications. However, parasitic inductance in the power loop can introduce resonance phenomena, impacting system stability and switching performance. To address this, this study integrates the parasitic parameters of printed circuit boards (PCBs) with the nonlinear junction capacitance characteristics of GaN devices. Finite element analysis (FEA) is employed to extract PCB parasitic inductance values and analyze their effects on GaN power-switching behavior. The findings indicate that precise extraction and analysis of parasitic inductance are critical for optimizing the performance of GaN switching devices. Additionally, this study investigates mitigation strategies to minimize parasitic inductance, ultimately enhancing GaN device design and reliability. The insights from this research provide valuable guidance for the development of GaN power devices in high-frequency applications. Full article

Accurate and efficient defect detection in printed circuit boards (PCBs) is critical for manufacturing quality control. Existing methods predominantly rely on manually extracted features such as surface texture, color, and shape for defect recognition and classification within small-dimensional feature datasets. A convolutional neural network (CNN) model was developed via transfer learning. Feature extraction involves diverse operations across different CNN layers. Essential features were selected, and dimensionality was reduced via either t-distributed stochastic neighbor embedding (t-SNE) or principal component analysis (PCA). Defect classification was subsequently performed by clustering the reduced features with either the K-means or K-nearest neighbors (KNN) algorithm. Compared with alternative model feature learning classifiers, the proposed small-dimensional CNN model performs significantly better. A defect recognition accuracy of 97.33% was achieved, with processing completed in approximately 60 s. This approach, which integrates transfer learning-based CNN feature extraction with dimensionality reduction and clustering techniques, provides a fast and effective method for high-precision defect detection and classification in PCBs. Full article

The escalating volume of waste electrical and electronic equipment (WEEE) poses a significant global environmental challenge. The disassembly of printed circuit boards (PCBs), a critical step for resource recovery, remains inefficient due to limitations in the adaptability and dexterity of existing automated systems. This paper proposes an intelligent disassembly system for PCB components that integrates a multimodal large language model (MLLM) with a multi-agent framework. The MLLM serves as the system’s cognitive core, enabling high-level visual-language understanding and task planning by converting images into semantic descriptions and generating disassembly strategies. A state-of-the-art object detection algorithm (YOLOv13) is incorporated to provide fine-grained component localization. This high-level intelligence is seamlessly connected to low-level execution through a multi-agent framework that orchestrates collaborative dual robotic arms. One arm controls a heater for precise solder melting, while the other performs fine “probing-grasping” actions guided by real-time force feedback. Experiments were conducted on 30 decommissioned smart electricity meter PCBs, evaluating the system on recognition rate, capture rate, melting rate, and time consumption for seven component types. Results demonstrate that the system achieved a 100% melting rate across all components and high recognition rates (90–100%), validating its strengths in perception and thermal control. However, the capture rate varied significantly, highlighting the grasping of small, low-profile components as the primary bottleneck. This research presents a significant step towards autonomous, non-destructive e-waste recycling by effectively combining high-level cognitive intelligence with low-level robotic control, while also clearly identifying key areas for future improvement. Full article

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions. Full article

This article describes the design, control, and implementation of a photovoltaic (PV) emulator using two-switch forward-converter topology. The system is designed to emulate the nonlinear electrical behavior of an actual PV panel under different environmental conditions including radiation level and temperature. The emulator provides galvanic isolation and also accurate current modulation to provide a safe yet reliable means of testing PV-related devices and algorithms within a laboratory setting. A dual-loop PI control is proposed to adjust the output current according to voltage feedback (VF), thus making accurate I–V and P–V curves achievable. Besides software simulation, a tailored printed circuit board (PCB) was fabricated. The simulation result demonstrated that the system can achieve a fast response and stable operation, with a maximum error percentage of about 2.1%, indicating high emulation fidelity, thereby providing an attractive platform for various evaluation purposes such as MPPT algorithms, inverters, and EMS. Full article

The electronic manufacturing industry is relying on automatic and rapid defect inspection of printed circuit boards (PCBs). Two main challenges hinder the accuracy and real-time defect detection: the growing density of electronic component placement and their size reduction, complicating the identification of tiny defects. This systematic review encompasses 56 relevant articles from the Scopus database between 2015 and the first quarter of 2025. This study examines deep learning (DL) architectures and machine learning (ML) algorithms for defect detection in PCB manufacturing. Findings indicate that 78.6% of the articles used models capable of detecting up to six defect types, and 62.5% relied on custom-made datasets. Convolutional neural networks (CNNs) are commonly utilized architectures due to their flexibility and adaptability to a variety of tasks. Still, real-time defect detection remains a challenge because of the complexity and high throughput in production settings. Likewise, accessible datasets are essential for the electronics industry to achieve broad adoption. Hence, architectures capable of learning and optimizing directly in the production line from unlabeled PCB data, without prior training, are necessary. Full article

This paper presents a 2.4/5.5 GHz single-stage dual-band low-noise amplifier (DB-LNA) based on a microstrip structure. The design utilizes a purely microstrip dual-band bias circuit (DBBC), composed of series microstrip lines and radial stubs. The broadband characteristics of the radial stubs enable wide frequency coverage and good frequency selectivity. A simple series-shunt microstrip matching network is adopted to maintain a compact overall design structure. The proposed DB-LNA is fabricated using a standard printed circuit board (PCB) process. Measurement results show that the amplifier achieves gains of 15.6 dB and 12.3 dB, input return losses of 14.6 dB and 14.5 dB, and output return losses of 23.2 dB and 14.1 dB at 2.4 GHz and 5.5 GHz, respectively. The measured noise figures (NF) are 1.0 dB and 1.1 dB at the corresponding frequencies, with −3 dB bandwidths exceeding 200 MHz. Compared with existing designs, the proposed LNA demonstrates notable advantages in both noise performance and bandwidth, while occupying a compact area of only 75 × 43 mm². Full article

This article presents a comprehensive analysis of the primary electrical power distribution system in hybrid-electric aircraft, with particular emphasis on its environmental performance assessed through Life Cycle Assessment (LCA). High-resolution Life Cycle Inventory (LCI) data were developed in collaboration with industry partners and refined to reflect current production standards. The results indicate that printed circuit boards (PCBs), magnets, precious metals (gold and silver), and copper are the primary contributors to environmental impact, with PCBs alone accounting for over 50% of material-related emissions. Although precious metals constitute only 0.014% of the product’s mass, they account for nearly 9% of total emissions due to the energy-intensive nature of their mining and refining processes. Additionally, manufacturing stages involving thermal treatments—such as surface coating of iron cores at 850 °C for 14 h—significantly increase energy consumption and associated emissions. The study concludes with recommendations for reducing the carbon footprint of future aircraft power systems through improved material efficiency, process optimization, and supply chain sustainability. Full article

This study aims to develop a chip-based five-electrode contactless conductivity detector for miniaturized ion chromatography (IC) systems. The detector comprises a detection chip (50 mm × 25 mm × 6 mm) and a detection circuit. The detection chip consists of a top layer, an insulating film, and a bottom layer wherein a planar five-electrode printed circuit board (PCB) is embedded. Among the five electrodes, one shielding electrode is designed to suppress the leakage current in the flow channel; consequently, the potential at the solution outlet is raised, further enhancing detection sensitivity. Furthermore, integrating the electrodes into a PCB module can reduce the difficulty of electrode fabrication and extend the lifespan of the electrodes. The detector was applied to a commercial IC system and successfully achieved the separation and detection of three anions (Cl⁻, NO₃⁻, SO₄²⁻). For standard solutions, the limit of detection (LOD) values of Cl⁻, NO₃⁻ and SO₄²⁻ are 0.47, 0.80, and 0.95 ppm, respectively. For mixed samples, the separation analysis was completed within 25 min, and the maximum detection error is no more than 2.2%. The five-electrode contactless detector developed shows great potential for application in miniaturized ion chromatography. Full article

Precise recognition and localization of electronic components on printed circuit boards (PCBs) are crucial for industrial automation tasks, including robotic disassembly, high-precision assembly, and quality inspection. However, strong visual interference from silkscreen characters, copper traces, solder pads, and densely packed small components often degrades the accuracy of deep learning-based detectors, particularly under complex industrial imaging conditions. This paper presents a two-stage, coarse-to-fine PCB component localization framework based on an optimized YOLOv11 architecture and a sub-pixel geometric refinement module. The proposed method enhances the backbone with a Convolutional Block Attention Module (CBAM) to suppress background noise and strengthen discriminative features. It also integrates a tiny-object detection branch and a weighted Bi-directional Feature Pyramid Network (BiFPN) for more effective multi-scale feature fusion, and it employs a customized hybrid loss with vertex-offset supervision to enable pose-aware bounding box regression. In the second stage, the coarse predictions guide contour-based sub-pixel fitting using template geometry to achieve industrial-grade precision. Experiments show significant improvements over baseline YOLOv11, particularly for small and densely arranged components, indicating that the proposed approach meets the stringent requirements of industrial robotic disassembly. Full article

The global production of electronic waste (e-waste) has increased due to the quick turnover of electronic devices, creating urgent problems for resource management and environmental sustainability. As a result, e-waste-derived materials (EWDMs) are being explored in pavement engineering research as sustainable substitutes in line with Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). Therefore, to assess global research production and the effectiveness of EWDMs in asphalt applications, this review combines scientometric mapping and systematic evidence synthesis. A total of 276 relevant publications were identified via a thorough search of Web of Science, Scopus, and ScienceDirect (2010–2025). These were examined via coauthorship structures, keyword networks, and contributions at the national level. The review revealed that China, India, and the United States are prominent research hubs. Additionally, experimental studies have shown that EWDMs, such as printed circuit board powder, fluorescent lamp waste glass, high-impact polystyrene, and acrylonitrile–butadiene–styrene, improve the fatigue life, Marshall stability, rutting resistance (up to 35%), and stiffness (up to 28%). However, issues with long-term field durability, microplastic release, heavy metal leaching, and chemical compatibility still exist. These restrictions highlight the necessity for standardised toxicity testing, harmonised mixed-design frameworks, and performance standards unique to EWDMs. Overall, the review shows that e-waste valorisation can lower carbon emissions, landfill build-up, and virgin material extraction, highlighting its potential in the circular pavement industry and promoting sustainable paving practices in accordance with SDGs 9, 11, 12, and 13. This review suggests that further studies on large-scale field trials, life cycles, and technoeconomic assessments are needed to guarantee the safe, long-lasting integration of EWDMs in pavements. It also advocates for coordinated research, supportive policies, and standardised methods. Full article

Printed Circuit Board (PCB) defect detection is critical for quality control in electronics manufacturing. Traditional manual inspection and classical Automated Optical Inspection (AOI) methods face challenges in speed, consistency, and flexibility. This paper proposes a CNN-based approach for automatic PCB defect detection using the YOLOv5 model. The method leverages a Convolutional Neural Network to identify various PCB defect types (e.g., open circuits, short circuits, and missing holes) from board images. In this study, a model was trained on a PCB image dataset with detailed annotations. Data augmentation techniques, such as sharpening and noise filtering, were applied to improve robustness. The experimental results showed that the proposed approach could locate and classify multiple defect types on PCBs, with overall detection precision and recall above 90% and 91%, respectively, enabling reliable automated inspection. A brief comparison with the latest YOLOv8 model is also presented, showing that the proposed CNN-based detector offers competitive performance. This study shows that deep learning-based defect detection can improve the PCB inspection efficiency and accuracy significantly, paving the way for intelligent manufacturing and quality assurance in PCB production. From a sensing perspective, we frame the system around an industrial RGB camera and controlled illumination, emphasizing how imaging-sensor choices and settings shape defect visibility and model robustness, and sketching future sensor-fusion directions. Full article

This article presents a comprehensive and intuitive analysis of the impact of packaging on diode performance and a two-step method for packaging parameter extraction. This is performed using a single forward bias point, one-port measurements and probe tips on a conventional printed circuit board (PCB). A PIN diode was used to validate the method, biased from reverse (−5 V) to forward (1.22 V) bias. Measurements were performed up to 27 gigahertz (GHz). The complete diode characterization process—from the design and the electrical modeling of the test fixture to the extraction of the unpackaged diode measurements—is detailed. The parameters of the package model were extracted, its effects were removed from the measurement, and the behavior of the unpackaged diode was determined. Three operating regions based on their radiofrequency and direct current (RF-DC) behavior were proposed, and an electrical model of the unpackaged diode was derived for each region. The results showed that the influence of the package caused the diode to remain in an unchanged behavior under different biases, indicating that it no longer rectified. The results presented herein are validated by the excellent correlation between the diode’s measured S-parameters, impedance, and admittance and their corresponding models. Full article

The surface defect detection of printed circuit boards (PCBs) plays a crucial role in the field of industrial manufacturing. However, the existing PCB defect detection methods have great challenges in detecting the accuracy of tiny defects under the complex background due to its compact layout. To address this problem, we propose a novel YOLO-AMBA-EASPP-BiFPN (YOLO-AEB) network based on the YOLOv10 framework that achieves high precision and real-time detection of tiny defects through multi-level architecture optimization. In the backbone network, an adaptive multi-branch attention mechanism (AMBA) is first proposed, which employs an adaptive reweighting algorithm (ARA) to dynamically optimize fusion weights within the multi-branch attention mechanism (MBA), thereby optimizing the ability to represent tiny defects under complex background noise. Then, an efficient atrous spatial pyramid pooling (EASPP) is constructed, which fuses AMBA and atrous spatial pyramid pooling-fast (ASPF). This integration effectively mitigates feature degradation while preserving expansive receptive fields, and the extraction of defect detail features is strengthened. In the neck network, the bidirectional feature pyramid network (BiFPN) is used to replace the conventional path aggregation network (PAN), and the bidirectional cross-scale feature fusion mechanism is used to improve the transfer ability of shallow detail features to deep networks. Comprehensive experimental evaluations demonstrate that our proposed network achieves state-of-the-art performance, whose F₁ score can reach 95.7% and mean average precision (mAP) can reach 97%, representing respective improvements of 7.1% and 5.8% over the baseline YOLOv10 model. Feature visualization analysis further verifies the effectiveness and feasibility of YOLO-AEB. Full article