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Make your own PCBs with Eagle : from schematic designs to finished boards
\"Fully updated coverage of PCB design and construction with EAGLE. This thoroughly revised, easy-to-follow guide shows, step-by-step, how to create your own professional-quality PCBs using the latest versions of EAGLE. Make your own PCBs with Eagle: from schematic designs to finished boards, Second Edition, guides you through the process of a developing a schematic, transforming it into a PCB layout, and submitting Gerber files to a manufacturing service to fabricate your finished board. Four brand-new chapters contain advanced techniques, tips, and features. Downloadable DIY projects include a sound level meter, Arduino shield, Raspberry Pi expansion board, and more!\"--Page 4 of cover.
Book
Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning
Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.
Journal Article
PCB Defect Detection via Local Detail and Global Dependency Information
Due to the impact of the production environment, there may be quality issues on the surface of printed circuit boards (PCBs), which could result in significant economic losses during the application process. As a result, PCB surface defect detection has become an essential step for managing PCB production quality. With the continuous advancement of PCB production technology, defects on PCBs now exhibit characteristics such as small areas and diverse styles. Utilizing global information plays a crucial role in detecting these small and variable defects. To address this challenge, we propose a novel defect detection framework named Defect Detection TRansformer (DDTR), which combines convolutional neural networks (CNNs) and transformer architectures. In the backbone, we employ the Residual Swin Transformer (ResSwinT) to extract both local detail information using ResNet and global dependency information through the Swin Transformer. This approach allows us to capture multi-scale features and enhance feature expression capabilities.In the neck of the network, we introduce spatial and channel multi-head self-attention (SCSA), enabling the network to focus on advantageous features in different dimensions. Moving to the head, we employ multiple cascaded detectors and classifiers to further improve defect detection accuracy. We conducted extensive experiments on the PKU-Market-PCB and DeepPCB datasets. Comparing our proposed DDTR framework with existing common methods, we achieved the highest F1-score and produced the most informative visualization results. Lastly, ablation experiments were performed to demonstrate the feasibility of individual modules within the DDTR framework. These experiments confirmed the effectiveness and contributions of our approach.
Journal Article
LW-YOLO: Lightweight Deep Learning Model for Fast and Precise Defect Detection in Printed Circuit Boards
Printed circuit board (PCB) manufacturing processes are becoming increasingly complex, where even minor defects can impair product performance and yield rates. Precisely identifying PCB defects is critical but remains challenging. Traditional PCB defect detection methods, such as visual inspection and automated technologies, have limitations. While defects can be readily identified based on symmetry, the operational aspect proves to be quite challenging. Deep learning has shown promise in defect detection; however, current deep learning models for PCB defect detection still face issues like large model size, slow detection speed, and suboptimal accuracy. This paper proposes a lightweight YOLOv8 (You Only Look Once version 8)-based model called LW-YOLO (Lightweight You Only Look Once) to address these limitations. Specifically, LW-YOLO incorporates a bidirectional feature pyramid network for multiscale feature fusion, a Partial Convolution module to reduce redundant calculations, and a Minimum Point Distance Intersection over Union loss function to simplify optimization and improve accuracy. Based on the experimental data, LW-YOLO achieved an mAP0.5 of 96.4%, which is 2.2 percentage points higher than YOLOv8; the precision reached 97.1%, surpassing YOLOv8 by 1.7 percentage points; and at the same time, LW-YOLO achieved an FPS of 141.5. The proposed strategies effectively enhance efficiency and accuracy for deep-learning-based PCB defect detection.
Journal Article
A non-printed integrated-circuit textile for wireless theranostics
While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI “nurse” for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.
The typical approach to electronics is to integrate sensors, power units, and controlling components on a printed circuit board (PCB). Here, the authors demonstrate a self-powered and fully integrated combination of sensors and controlling components that is woven, rather than integrated onto a PCB, allowing for wearable health monitoring.”
Journal Article
Detection of solder paste defects with an optimization‐based deep learning model using image processing techniques
Purpose
In the production processes of electronic devices, production activities are interrupted due to the problems caused by soldering defects during the assembly of surface-mounted elements on printed circuit boards (PCBs), and this leads to an increase in production costs. In solder paste applications, defects that may occur in electronic cards are usually noticed at the last stage of the production process. This situation reduces the efficiency of production and causes delays in the delivery schedule of critical systems. This study aims to overcome these problems, optimization based deep learning model has been proposed by using 2D signal processing methods.
Design/methodology/approach
An optimization-based deep learning model is proposed by using image-processing techniques to detect solder paste defects on PCBs with high performance at an early stage. Convolutional neural network, one of the deep learning methods, is trained using the data set obtained for this study, and pad regions on PCB are classified.
Findings
A total of six types of classes used in the study consist of uncorrectable soldering, missing soldering, excess soldering, short circuit, undefined object and correct soldering, which are frequently used in the literature. The validity of the model has been tested on the data set consisting of 648 test data.
Originality/value
The effect of image processing and optimization methods on model performance is examined. With the help of the proposed model, defective solder paste areas on PCBs are detected, and these regions are visualized by taking them into a frame.
Journal Article
Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks
In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.
Journal Article
Materials and micro drilling of high frequency and high speed printed circuit board: a review
The high-frequency and high-speed printed circuit board (PCB) with lower transmission loss, higher heat resistance, and better processability play increasing significant roles in mobile communication technology. However, because the materials and micro drilling process of high-frequency and high-speed PCB are very different from the traditional printed board, there are still many of key techniques to be explored in the future study. In this paper, the characteristics of high-frequency and high-speed PCB were presented. Researches concerning the design and wear ability of micro drill, the analysis of micro drilling force and temperature, and the quality of micro holes were reviewed. Finally, several key techniques and challenges regarding materials and micro drilling were suggested.
Journal Article
Bioleaching of Typical Electronic Waste—Printed Circuit Boards (WPCBs): A Short Review
The rapid pace of innovations and the frequency of replacement of electrical and electronic equipment has made waste printed circuit boards (WPCB) one of the fastest growing waste streams. The frequency of replacement of equipment can be caused by a limited time of proper functioning and increasing malfunctions. Resource utilization of WPCBs have become some of the most profitable companies in the recycling industry. To facilitate WPCB recycling, several advanced technologies such as pyrometallurgy, hydrometallurgy and biometallurgy have been developed. Bioleaching uses naturally occurring microorganisms and their metabolic products to recover valuable metals, which is a promising technology due to its cost-effectiveness, environmental friendliness, and sustainability. However, there is sparse comprehensive research on WPCB bioleaching. Therefore, in this work, a short review was conducted from the perspective of potential microorganisms, bioleaching mechanisms and parameter optimization. Perspectives on future research directions are also discussed.
Journal Article