Printed Circuit Board Inspection Research Articles

A PCB image segmentation model based on rotational X-ray computed laminography imaging.

The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, with one integrating edge diffusion and l0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model.

Implementation of quality control systems based on machine vision and web technologies

The development of Computer Vision technologies has seen widespread adoption in various fields over the last time, including medicine, manufacturing, transportation, logistics, agriculture, and chemical industry. Research analysis indicates that Computer Vision and its practical implementation, known as Machine Vision, are widely used for defect detection in production and equipment. Despite significant research progress in this field in recent years, many unresolved issues remain concerning the quality and dimensions of input data, selection of image recognition models and their training, and the accuracy of results. Integration of machine vision system elements for information exchange and decision-making poses a significant challenge. This paper presents one approach to addressing this issue. The aim of the paper is to explore the implementation approach of quality control systems using web technologies. The task of detecting defects in printed circuit boards (PCBs) is common in many manufacturing processes. PCB inspection involves comparing the current PCB sample with a defect-free reference. Essentially, this task involves comparing two images and identifying differences between them. Defect recognition is based on the Structural Similarity Index (SSIM), which computes the degradation component reflecting the difference between images and the similarity component. The SSIM metric is already implemented in image processing libraries. This work utilizes calculations using OpenCV and scikit-image image libraries. To automate the PCB comparison process and address deployment and integration challenges at the SCADA level, a web application has been developed. This web application allows users to select a reference PCB image and upload the current PCB image for inspection. For the client-side implementation, Vue.js was chosen due to its popularity and efficiency in developing web interfaces, particularly Single Page Applications (SPAs). Vue.js's reactive system ensures automatic updates to reflect changes in data state. For the server-side implementation, Flask – a Python framework, was used, facilitating integration with the PCB comparison algorithm as both utilize Python. According to the available features, operators can upload two images – a reference PCB image and the PCB image to be compared with the reference. After uploading, the images are displayed in the system interface. On the server side, the received data is decoded and compared using the appropriate algorithms. After comparison, the results are encoded in base64 format and sent back to the client-side for display, allowing the operator to assess the similarity or differences between the PCBs. The application can operate in both automatic and manual modes. Therefore, an analysis of the challenges encountered in implementing and deploying quality control systems based on computer vision methods has been developed. The use of web technologies, particularly Python frameworks, for integrating developed models is proposed. This approach is demonstrated using the example of PCB defect recognition tasks.

Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.

Enhancement of Product-Inspection Accuracy Using Convolutional Neural Network and Laplacian Filter to Automate Industrial Manufacturing Processes

The automation of the manufacturing process of printed circuit boards (PCBs) requires accurate PCB inspections, which in turn require clear images that accurately represent the product PCBs. However, if low-quality images are captured during the involved image-capturing process, accurate PCB inspections cannot be guaranteed. Therefore, this study proposes a method to effectively detect defective images for PCB inspection. This method involves using a convolutional neural network (CNN) and a Laplacian filter to achieve a higher accuracy of the classification of the obtained images as normal and defective images than that obtained using existing methods, with the results showing an improvement of 11.87%. Notably, the classification accuracy obtained using both a CNN and Laplacian filter is higher than that obtained using only CNNs. Furthermore, applying the proposed method to images of computer components other than PCBs results in a 5.2% increase in classification accuracy compared with only using CNNs.

Development of a real-time Printed Circuit board (PCB) visual inspection system using You Only Look Once (YOLO) and fuzzy logic algorithms

Automated visual inspection on PCB boards is a critical process in electronic industries. Misalignment component detection is one of the challenging tasks in the PCB inspection process. Defects during the production process might include missing and misaligned components as well as poor solder connections. Inspection of PCB is therefore required to create practically defect-free products. There are various methods have been developed to perform this task in literature. The significance of this research is to propose an efficient with low-cost system is still require in small scale manufacturing to perform the misalignment or missing component detection on PCB boards. However, an efficient, low-cost system is still required in small-scale manufacturing to perform the misalignment or missing component detection on PCB boards. In this study, a real-time visual inspection system is developed for misalignment component detection. The proposed system consists of hardware and software frameworks. The hardware framework involves the setup of devices and modules. The software framework is composed of pre-processing and post-processing. In pre-processing, image enhancement is applied to remove noises from captured images and You Only Look Once (YOLO) object detector for components detection. Subsequently, the detected components are compared to the corresponding defined pattern using a template-matching algorithm. As experimental shown, the proposed system satisfies the requirement of missing component detection on PCB boards.

Printed circuit board inspection using computer vision

Printed circuit board inspection using computer vision

Automated layer identification and segmentation of x‐ray computer tomography imaged PCBs

AbstractThe non‐destructive inspection of Printed Circuit Boards (PCBs) through r‐ray Computer Tomography (CT) is a recently developed method that offers several advantages over traditional inspection techniques. This method is non‐invasive, quick, and offers high resolution, leading to significant improvements in inspection and repair efficiency. Post‐image analysis is an important step in PCB inspection and has important practical significance for automatic positioning and determining the location of faults. Usually, the results of image segmentation are an important basis for PCB defect detection, and accurate segmentation results can effectively improve the efficiency and accuracy of PCB inspection and increase the level of automation. This paper discusses two innovative improvements for the automatic segmentation process: firstly determining which slices of an x‐ray CT 3D PCB stack belong to which layer on a physical PCB in an automatic, generic and completely unsupervised way, which is verified on a 4‐layer PCB; secondly proposing a level set‐based image segmentation algorithm for the problem of gray scale inhomogeneity present in PCB CT. Experimental results on real PCB CT images with high aliasing and artifacts show that the proposed model can obtain better performance than the popular active contour models.

A deep context learning based PCB defect detection model with anomalous trend alarming system

The quality of a printed circuit board (PCB) is paramount towards ensuring proper functionality of electronic products. To achieve the required quality standards, substantial research and development efforts were invested to automate PCB inspection for defect detection, primarily using computer vision techniques. Despite these advancements, the accuracy of such techniques is often susceptible towards varying board and component size. Efforts to increase its accuracy especially for small or tiny defects on a PCB often lead to a tradeoff with reduced real-time performance, which in turn limits its applicability in the manufacturing industry. Hence, this paper puts forward an enhanced deep learning network which addresses the difficulty in inferring tiny or varying defects on a PCB in real-time. Our proposed enhancements consist of i) A novel multi-scale feature pyramid network to enhance tiny defect detection through context information inclusion; and ii) A refined complete intersection over union loss function to precisely encapsulate tiny defects. Experimental results on a publicly available PCB defects dataset demonstrate that our model achieves 99.17% mean-average precision, while maintaining real-time inferencing speed at 90 frames per second. In addition, we introduce three trend detection algorithms which alert an operator when abnormal development of defect characteristics is detected. Each algorithm is responsible for localizing defect buildups, increasing defect size and increasing defect occurrences, respectively. As a whole, the proposed model is capable of performing accurate and reliable real-time PCB inspection with the aid of an automated alert capability. The dataset and trained models are available at: https://github.com/JiaLim98/YOLO-PCB.

A Deep Regression Framework Toward Laboratory Accuracy in the Shop Floor of Microelectronics

Deep learning (DL) has certainly improved industrial inspection, while significant progress has also been achieved in metrology with impressive results reached through their combination. However, it is not easy to deploy metrology sensors in a factory, as they are expensive, and require special acquisition conditions. In this article, we propose a methodology to replace a high-end sensor with a low-cost one introducing a data-driven soft sensor (SS) model. Concretely, a residual architecture (R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> esNet) is proposed for quality inspection, along with an error-correction scheme to lessen noise impact. Our method is validated in printed circuit board (PCB) manufacturing, through the identification of defects related to glue dispensing before the attachment of silicon dies. Finally, a detection system is developed to localize PCB regions of interest, thus offering flexibility during data acquisition. Our methodology is evaluated under operational conditions achieving promising results, whereas PCB inspection takes a fraction of the time needed by other methods.

Development of hybrid optical sensor based on deep learning to detect and classify the micro-size defects in printed circuit board

Testing with naked eye contact on large-sized printed circuit boards (PCBs) mass produced is exhausting and time consuming. Since the eyes will get tired during the long-term test phase, there is a possibility of erroneous measurement. In addition, for a quality PCB inspection, identifying the defective location on the PCBs and classifying the types of these defects is an important step in the production process. In this study, we propose a new hybrid optical sensor (HOS) based on deep learning (DL) to sense the micro-size defects on PCBs. In this context, the combination of the lateral shearing digital holographic microscopy (LSDHM) and the microscopic fringe projection profilometry (MFPP) is proposed. Another aim of ours is to classify the PCBs defects by using deep learning classifier based on Convolution Neural Network (CCN) algorithm. Contrast to the complex systems used in the literature for sensing the PCB defects, an optical microscopic sensor with minimum components has been proposed to use for the first time. Thanks to proposed HOS based on DL, we reveal the successful results obtained with high accuracy (99%) that the defects on PCBs can be detected and classified in very short time with non-contact and real-time imaging. Hence, the problem of time consuming has been overcome. It is thought that the proposed hybrid sensor will lead to future studies in terms of the early detection of the micro-size defects on PCBs with high performance before mass production process.

A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN

Normally functioning and complete printed circuit board (PCB) can ensure the safety and reliability of electronic equipment. PCB defect detection is extremely important in the field of industrial inspection. For traditional methods of PCB inspection, such as contact detection, are likely to damage the PCB surface and have high rate of erroneous detection. In recent years, methods of detection through image processing and machine learning have gradually been put into use. However, PCB inspection is still an extremely challenging task due to the small defects and the complex background. To solve this problem, a lightweight one-stage defect detection network based on dual attention mechanism and Path Aggregation Feature Pyramid Network (PAFPN) has been proposed. At present, some methods of defect detection in industrial applications are often based on object detection algorithms in the field of deep learning. Through comparative experiments, compared with the Faster R-CNN and YOLO v3 which are usually used in the current industrial detection, the inference time of our method are reduced by 17.46 milliseconds (ms) and 4.75 ms, and the amount of model parameters is greatly reduced. It is only 4.42 M, which is more suitable for industrial fields and embedded development systems. Compared with the common one-stage object detection algorithm Fully Convolutional One-Stage Object Detection (FCOS), mean Average Precision (mAP) is increased by 9.1%, and the amount of model parameters has been reduced by 86.12%.

Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder.

As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images.

Research on micro-CL geometric errors

Computed laminography (CL) is a nondestructive testing technique for planar objects. It has been applied in applications like printed circuit board inspection and paleontological fossils research. However, the CL system calibration is a challenging task for complex mechanical structure and many degrees of freedom. To evaluated the influence of geometric errors, a new-type microscopic CL (micro-CL) with different kinds of geometric errors was simulated. The results can guide the installation and calibration of a micro-CL scanner.

Semantic Segmentation of a Printed Circuit Board for Component Recognition Based on Depth Images.

Locating and identifying the components mounted on a printed circuit board (PCB) based on machine vision is an important and challenging problem for automated PCB inspection and automated PCB recycling. In this paper, we propose a PCB semantic segmentation method based on depth images that segments and recognizes components in the PCB through pixel classification. The image training set for the PCB was automatically synthesized with graphic rendering. Based on a series of concentric circles centered at the given depth pixel, we extracted the depth difference features from the depth images in the training set to train a random forest pixel classifier. By using the constructed random forest pixel classifier, we performed semantic segmentation for the PCB to segment and recognize components in the PCB through pixel classification. Experiments on both synthetic and real test sets were conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that our method can segment and recognize most of the components from a real depth image of the PCB. Our method is immune to illumination changes and can be implemented in parallel on a GPU.

Quality Analysis of Printed Circuit Board Track and Soldering

Bare Printed Circuit Board (PCB) is a circuit composed with non-placement of electronic components. Quality checking plays an important role in the manufacturing industry. In order to help the small scale industry, students and researchers to identify the defects, this paper presents an efficient algorithm for an automated visual PCB inspection system that will automatically detect any defect on PCBs. The defects are been detected by image subtraction algorithm. This algorithm utilizes a template and a defective image of a bare PCB with same size variations and orientations. There are several defects presented in a PCB. Defects like missing hole, open circuit, short circuit is been identified using the automated PCB inspection system.

Study and application of improved level set method with prior graph cut in PCB image segmentation

Purpose The segmentation of printed circuit board (PCB) images is an important process in PCB inspection. The circuit traces, pads and vias in a PCB are dense and curved, and the PCB image obtained using different cameras or in different conditions may exhibit a large image gradient, which leads to inaccuracy and inefficiency in the PCB image segmentation. This paper aims to propose an improved local binary fitting level set method with prior graph cut, aiming to improve the accuracy and efficiency of the segmentation of PCB images obtained using different cameras or in different environments. Design/methodology/approach First, the paper constructs a 4-connected undirected graph using a given PCB image and classifies it based on the graph cut. Second, an adaptive initialization level set is implemented using the priori information obtained from the graph cut. Finally, the paper constructs a priori energy term using the prior information and introduces it into the energy function of the level set. Findings The approach results in an improved accuracy of segmentation in the context of a large gradient within the image. Experimental results demonstrate that the method can solve the deviation of artificially initialized level set from targets and improve the efficiency and accuracy of segmentation. Research limitations/implications This study only considers level set method as the research object. Iteration of the level set method takes a long time for a given huge PCB picture, which makes it impossible to apply to scenes with high real-time requirements. Practical implications PCB image segmentation is an important process in the PCB inspection. Since template matching and morphology techniques are well-established, image segmentation quality has a significant impact on the accuracy of detection. Originality/value This paper studies the segmentation of PCB images, improves the efficiency and accuracy of segmentation and facilitates the subsequent applications, such as in the nondestructive testing of PCB.

Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest

With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB), which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF), then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE) map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method.

PCB Fault Detection Using Image Processing

The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment where delivery of 100% defect free PCBs is the expectation. To meet such expectations, identifying various defects and their types becomes the first step. In this PCB inspection system the inspection algorithm mainly focuses on the defect detection using the natural images. Many practical issues like tilt of the images, bad light conditions, height at which images are taken etc. are to be considered to ensure good quality of the image which can then be used for defect detection. Printed circuit board (PCB) fabrication is a multidisciplinary process, and etching is the most critical part in the PCB manufacturing process. The main objective of Etching process is to remove the exposed unwanted copper other than the required circuit pattern. In order to minimize scrap caused by the wrongly etched PCB panel, inspection has to be done in early stage. However, all of the inspections are done after the etching process where any defective PCB found is no longer useful and is simply thrown away. Since etching process costs 0% of the entire PCB fabrication, it is uneconomical to simply discard the defective PCBs. In this paper a method to identify the defects in natural PCB images and associated practical issues are addressed using Software tools and some of the major types of single layer PCB defects are Pattern Cut, Pin hole, Pattern Short, Nick etc., Therefore the defects should be identified before the etching process so that the PCB would be reprocessed. In the present approach expected to improve the efficiency of the system in detecting the defects even in low quality images

Bare Printed Circuit Board Inspection Using Image Analysis

Bare Printed Circuit Board Inspection Using Image Analysis

Planar cone-beam computed tomography with a flat-panel detector

For a dedicated x-ray inspection of printed-circuit boards (PCBs), a bench-top planar cone-beam computed tomography (pCT) system with a flat-panel detector has been built in the laboratory. The system adopts the tomosynthesis technique that can produce cross-sectional images parallel to the axis of rotation for a limited angular range. For the optimal operation of the system and further improvement in the next design, we have evaluated imaging performances, such as modulation-transfer function, noise-power spectrum, and noise-equivalent number of quanta. The performances are comparatively evaluated with the coventional cone-beam CT (CBCT) acquisition for various scanning angular ranges, applied tube voltages, and geometrical magnification factors. The pCT scan shows a poorer noise performance than the conventional CBCT scan because of less number of projection views used for reconstruction. However, the pCT shows a better spatial-resolution performance than the CBCT. Because the image noise can be compensated by an elevated exposure level during scanning, the pCT can be a useful modality for the PCB inspection that requires higher spatial-resolution performance.