Search Results (11)

The rapid miniaturization and increasing complexity of Printed Circuit Boards (PCBs) have rendered traditional automated optical inspection (AOI) systems inadequate for high-precision defect detection. While deep learning approaches have shown promise, they often struggle to balance the need for high-resolution feature extraction with the computational efficiency required for real-time industrial deployment. Furthermore, the “black-box” nature of most convolutional neural networks (CNNs) limits their adoption in stringent quality assurance environments where interpretability is paramount. To address these challenges, this paper proposes PCBVisionNet, a novel, lightweight deep learning architecture specifically engineered for automated multi-class PCB defect classification. The framework integrates a Dual-Domain Attention Mechanism (DDAM) and a Multi-Scale Feature Extractor (MSFE) with residual learning to effectively capture both microscopic anomalies and complex structural defects, enabling robust image-level classification of PCB defect categories. We evaluate the proposed model on three publicly available datasets: DeepPCB, PKU-Market-PCB, and HRIPCB. Experimental results demonstrate that PCBVisionNet achieves superior classification performance with a mean Average Precision (mAP) of 99.4% across defect categories, outperforming state-of-the-art architectures such as ResNet50, EfficientNet-B0, and Vision Transformers, while requiring 45% fewer parameters and reducing inference time by 23 ms per image. The integration of Gradient-weighted Class Activation Mapping (Grad-CAM) provides post hoc visual explainability, highlighting image regions that influence the classification decision to support interpretability and root-cause analysis. The proposed framework offers a highly accurate, efficient, and interpretable solution for modern smart manufacturing systems. Full article

The relentless miniaturization of semiconductor components and Printed Circuit Boards (PCBs) has rendered Automated Optical Inspection (AOI) of micro-defects a critical bottleneck in modern manufacturing and metrology. While in-line inspection systems offer economically viable and scalable quality control solutions, they impose stringent constraints on both inference latency and detection robustness—particularly for diminutive, sparsely distributed defects (e.g., mouse bites, pinholes) amidst complex, repetitive circuit topologies. To bridge this gap, we present a semantic-enhanced FCOS framework specifically engineered for micro-defect inspection. Our approach introduces two synergistic innovations: (1) a Semantic-Guided Upsampling Unit (SGU) that adaptively reweights channel–spatial features to reconcile the semantic disparity between shallow textural details and deep contextual representations; and (2) a Sparse Center-ness Calibration (SCC) module that enforces high-confidence, spatially sparse supervision to sharpen localization precision and suppress false positives. The SGU is integrated within a Progressive Semantic-Enhanced Feature Pyramid Network (PSE-FPN) that extends multi-scale representations to stride-4 (P2) resolution, while the SCC module is embedded directly into the detection head. Comprehensive evaluations on MS COCO and the real-world DeepPCB dataset validate the efficacy of our design. On COCO, our model achieves 41.8% AP with real-time throughput of 28 FPS on a single NVIDIA 1080Ti GPU. A lightweight variant further attains 41.6% AP at 42 FPS, accommodating high-throughput production environments. For PCB defect detection, the framework delivers 98.7% mAP@0.5, substantially outperforming contemporary detectors. These results demonstrate that semantics-aware, lightweight architectures enable scalable, real-time quality assurance in semiconductor manufacturing. 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

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing. Full article

Printed Circuit Board (PCB) manufacturing demands accurate defect detection to ensure quality. Traditional methods, such as manual inspection or basic automated object inspection systems, are often time-consuming and inefficient. This work presents a deep learning architecture using Faster R-CNN with a ResNet-50 backbone to automatically detect and classify PCB defects, including Missing Holes (MHs), Open Circuits (OCs), Mouse Bites (MBs), Shorts, Spurs, and Spurious Copper (SC). The designed architecture involves data acquisition, annotation, and augmentation to enhance model robustness. In this study, the CNN-Resnet 50 backbone achieved a precision–recall value of 87%, denoting strong and well-balanced performance in PCB fault detection and classification. The model effectively identified defective instances, reducing false negatives, which is critical for ensuring quality assurance in PCB manufacturing. Performance evaluation metrics indicated a mean average precision (mAP) of 88% and an Intersection over Union (IoU) score of 72%, signifying high prediction accuracy across various defect classes. The developed model enhances efficiency and accuracy in quality control processes, making it a promising solution for automated PCB inspection. Full article

Printed circuit board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving, so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods; however, they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduces the number of trainable ML parameters and, thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection. The study results indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities. Full article

A printed circuit board (PCB) is an essential element for practical circuit applications and its failure can inflict large financial costs and even safety concerns, especially if the PCB failure occurs prematurely and unexpectedly. Understanding the failure modes and even the failure mechanisms of a PCB failure are not sufficient to ensure the same failure will not occur again in subsequent operations with different batches of PCBs. The identification of the root cause is crucial to prevent the reoccurrence of the same failure. In this work, a step-by-step approach from customer returned and inventory reproduced boards to the root cause identification is described for an actual industry case where the failure is a PCB burn-out. The failure mechanism is found to be a conductive anodic filament (CAF) even though the PCB is CAF-resistant. The root cause is due to PCB de-penalization. A reliability verification to assure the effectiveness of the corrective action according to the identified root cause is shown to complete the case study. This work shows that a CAF-resistant PCB does not necessarily guarantee no CAF and PCB processes can render its CAF resistance ineffective. Full article

Companies working on semiconductors must currently assure the customers of not only the performance of the semiconductor device per se, but also its performance when it is implemented in a real board, therefore including the role of parasitic effects. It is therefore very important to evaluate, especially during the design phase, not only the single device, but the complete board and their mutual interactions. This consideration opens a new area of investigation in the field of electronic systems engineering. In the current literature, the problem of a software evaluation of parasitic dynamics and electromagnetic effects on printed boards is addressed from the point of view of researchers. Moreover, it is fundamental to have a complete view of the various tools that could be usefully adopted from the perspective of manufacturers. This is the main motivation of this technical note, which performs a comparative analysis of the most prominent software tools for printed circuit boards’ (PCBs) simulation. The main features, the key aspects, and the limitations of the software packages are analyzed in terms of the industrial design of power electronics devices, in order to ensure efficiency and fastness in the semiconductor market. Full article

The structural safety of solar cells mounted on deployable solar panels in the launch vibration environment is a significant aspect of a successful CubeSat mission. This paper presents a novel highly damped deployable solar panel module that is effective in ensuring structural protection of solar cells under the launch environment by rapidly suppressing the vibrations transmitting through the solar panel by constrained layer damping achieved using printed circuit board (PCB)-based multilayered thin stiffeners with double-sided viscoelastic tapes. A high-damping solar panel demonstration model with a three-pogo pin-based burn wire release mechanism was fabricated and tested for application in the 6U CubeSat “STEP Cube Lab-II” developed by Chosun University, South Korea. The reliable release function and radiation hardness assurance of the mechanism in an in-orbit environment were confirmed by performing solar panel deployment tests and radiation tests, respectively. The design effectiveness and structural safety of the proposed solar panel module were validated by launch vibration and in-orbit environment tests at the qualification level. Full article

In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance. Full article

In this paper, a mid-range wireless power transfer (WPT) system based on metamaterials (MMs) has been presented. It has been shown that the MMs are positioned in the WPT system to focalize the electromagnetic field for distance enhancement and efficiency improvement theoretically and experimentally. The MMs were fabricated by using a single layer printed-circuit board (PCB) with the negative magnetic permeability, μ_r. An applicable impedancetuning technology was implemented by changing the operating distance between the drive (load) resonator and the internal resonator, which can achieve the optimal load of the system. In addition, the Class-E RF (radio frequency) power amplifier is firstly proposed as the high frequency excitation source of the WPT system based on the MMs due to its simple design and high efficiency. The proposed technology can achieve efficiency improvements of 4.26% and 9.13% at distances of 100 cm and 200 cm around the 2.80 MHz WPT system with the MMs, respectively. Specially, it is worth mentioning that the system efficiency is enhanced by 18.58% at 160 cm. The measured results indicate the WPT system based on the MMs can assure a stable output power of 5W at a transfer distance of 200 cm. Full article