Defects In Printed Circuit Boards Research Articles

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.

PCB defect detection system based on image processing

Under the background of intelligent manufacturing, the quality inspection of printed circuit board (PCB), as the carrier of electronic products, plays an important role. In view of the shortcomings of existing traditional detections, this paper has put forward a PCB detection method based on machine vision, focusing on image preprocessing algorithm and detection analysis. The median filter algorithm was used to remove the noise in the image. Logarithmic transformation was used to enhance image contrast. Sobel operator was used for edge detection to obtain the geometric center of the defect area. On this basis, the PCB image was registered by template matching method, and the difference between the preprocessed PCB image to be tested and the standard PCB image was calculated to get the difference map. Subsequently, morphological algorithm was used to remove the slight differences and obtain the defect map. In the end, the simulation results have shown that the proposed algorithm has good detection effect and high precision in PCB defect detection.

Semi-Supervised Defect Detection Method with Data-Expanding Strategy for PCB Quality Inspection.

Printed circuit board (PCB) defect detection plays a crucial role in PCB production, and the popular methods are based on deep learning and require large-scale datasets with high-level ground-truth labels, in which it is time-consuming and costly to label these datasets. Semi-supervised learning (SSL) methods, which reduce the need for labeled samples by leveraging unlabeled samples, can address this problem well. However, for PCB defects, the detection accuracy on small numbers of labeled samples still needs to be improved because the number of labeled samples is small, and the training process will be disturbed by the unlabeled samples. To overcome this problem, this paper proposed a semi-supervised defect detection method with a data-expanding strategy (DE-SSD). The proposed DE-SSD uses both the labeled and unlabeled samples, which can reduce the cost of data labeling, and a batch-adding strategy (BA-SSL) is introduced to leverage the unlabeled data with less disturbance. Moreover, a data-expanding (DE) strategy is proposed to use the labeled samples from other datasets to expand the target dataset, which can also prevent the disturbance by the unlabeled samples. Based on the improvements, the proposed DE-SSD can achieve competitive results for PCB defects with fewer labeled samples. The experimental results on DeepPCB indicate that the proposed DE-SSD achieves state-of-the-art performance, which is improved by 4.7 mAP at least compared with the previous methods.

Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters

Increasingly high demands are being placed on the quality inspection of printed circuit boards (PCBs). A full surface inspection of all produced PCBs and a high defect detection accuracy of the inspection system are becoming prerequisites for an efficient quality management. At the same time, the demand for PCBs is constantly increasing over the years due to the high demand for electrical devices. Human inspection is no longer feasible due to the high production rates and required defect detection accuracy. Therefore, automatic inspection systems are increasingly used for quality control in the various process steps of PCB production. In this article, the first automatic inspection system for detecting defects on Electroless Nickel Immersion Gold (ENIG) and Electroless Nickel Immersion Palladium Immersion Gold (ENIPIG) surfaces is presented. A pretrained convolutional neural network (CNN) and the sliding window approach are used. A training dataset consisting of six different defect types and an OK class containing only defect-free PCB images was labeled for this classification problem. The hyperparameters learning rate and batch size are varied for different training runs of the CNN, and the performance of the network in PCB defect detection is evaluated using a test dataset. The true-positive rate, truenegative rate, and F1-score were analyzed for the evaluation. Our results show that the best performances could be achieved at low batch sizes and low learning rates.

Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters

Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters

Defect Detection in Printed Circuit Boards Using Leaky-LeNet 5

Each electronic device includes printed circuit boards (PCBs), where defect detection is an important process to enhance the quality of PCB production. To accomplish error-free PCBs, the researchers and experts converted traditional manual inspections into automated systems. The manual inspection results are ineffective, where the non-defective PCBs are classified as defective PCBs. A subsequent study added a technique called LeNetwork-5 (LeNet-5) and speeded up robust feature extraction (SURF) techniques to identify defects. The existing method was unreliable, so further research was conducted in this area using Leaky techniques. Two objectives are achieved using SURF technique: (1) registration of the PCB to be checked against a reference PCB and (2) identification of feature points on the PCB helps to find missing components. Additionally, the Leaky LeNet-5 classifier is employed to classify the defects in PCBs. The proposed method achieved accuracy of 99.83%, sensitivity of 96.26%, specificity of 99.15%, and F1-Score of 98.45%.

End-to-end deep learning framework for printed circuit board manufacturing defect classification

We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.

Multiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection

Multiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection

YOPCB: A Single-stage Multi-attention Detection Network for PCB Surface Defects

With the miniaturization of electronic components, printed circuit boards (PCB) as a important component of them has become more and more complex and exquisite. Different defects in PCB can be found in modern industrial manufacture, such as lack of weld, lacking components, continuous welding, and the color ring resistance, resulting in low production. Accordingly, it is necessary to achieve the high rate of precision and efficiency when defecting during PCBs industrial production process. Considering that existing detection of PCB defects have a low efficiency, we propose an improved model YOPCB defecting PCB surface defects. Our improved model can achieve the precision of 97.6% mAP, which means our method is more efficient.

Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion

In printed circuit board (PCB) defect detection, it is difficult to collect defect samples, and the detection effect is poor due to the lack of data. On the basis of the few-shot learning method, a few-shot PCB defect detection model is proposed. This model introduces feature enhancement module and multi-scale fusion module. The feature enhancement module based on the improved convolution block attention module (CBAM) can highlight the key areas of the received feature maps and suppress the interference of useless information. Aiming at the small size of PCB defects, a multi-scale feature fusion strategy is proposed. It can extract multi-scale feature maps of PCB and fuse them into a high-quality feature map containing different scale information, which can improve the detection precision of the model for small object defects. A large number of experiments on PCB dataset show that our few-shot PCB defect detection model outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30). Notably, the proposed model can take into account both detection efficiency and precision, which means it has high practical application value.

LPViT: A Transformer Based Model for PCB Image Classification and Defect Detection

PCB (printed circuit board) is an extremely important component of all electronic products, which has greatly facilitated human life. Meanwhile, tons of PCBs in the waste streams become a waste of resources, which puts the recycling and reuse of PCBs in urgent need. In the manufacturing and recycling of electronic products, the classification of PCBs, recognition of sub-components, and defect detection have been the key technology. Traditional manual detection and classification are subjective and rely on individuals’ experience. With the development of artificial intelligence, lots of research efforts have been dedicated to the automated detection and recognition of PCBs. In this paper, we propose a transformer-based model, LPViT, for defect detection and classification of PCBs. We conduct the defect detection task on the dataset DeepPCB, which consists of six different types of PCB defects. Defect detection benefits both manufacturing and recycling of PCBs. Among many electronic products, a group of affordable, general-purpose, and small-size PCBs is very popular, which are referred to as micro-PCBs. The classification and recognition of those PCBs will greatly facilitate the recycling and reuse process. We conduct the classification task on a dataset called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">micro-PCB</b> , which includes 12 types of popular, general-purpose, affordable, and small PCBs. Through comparative experiments, our system demonstrates its advantage in both classification and defect detection tasks.

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.

PCB Defect Detection Method Based on Transformer-YOLO

In order to solve the problem of low accuracy and efficiency in printed circuit board(PCB) defect detection using reference methods, a Transformer-YOLO network detection model is proposed. Firstly, an imporved clustering algorithm is used to generate the anchor box suitable for the PCB defect data set of this paper. Secondly, abandoning the traditional idea of using convolutional neural network to extract image feature, Swin Transformer is used as the feature extraction network, which can effectively establish the dependency between image features. Finally, to modify the order of the channels in the feature map and enable the network to more effectively focus on the information with greater value, the convolution and attention mechanism module is added to the feature detection network component. Comparing the network model proposed in this paper with Faster R-CNN, SSD, YOLOv3, YOLOv4 and YOLOv5, the experimental results show that the proposed model improves the accuracy by 23.90%, 15.51%, 10.70%, 7.83% and 6.12% respectively, which is better than other most mainstream target detection models and has relatively small volume.

Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks

Since printed circuit board (PCB) is the key to ensure the reliability of electronic equipment. Therefore, defect detection for PCB is a basic and necessary work. This paper proposes a PCB defect detection method based on an improved fully convolutional neural networks to detect four types of defects: spurs, mouse bites, short circuits and open circuits. The improved neural networks increase the receptive field and solve the problems of ordinary atrous convolution information loss of continuity by introducing the continuous atrous convolution module. At the same time, we introduce the improved skip layer after the upsampling layer to get the characteristics of multi-scale fusion, improve the resolution of the image. Due to the fewer levels of the whole networks, MobileNet-V2 networks model is adopted in the improved fully convolutional neural networks, which is compared with ResNet-50 and Vgg-16 networks model. Comparison of accuracy of four kinds of PCB classification defects. Experimental results show that the improved fully convolutional neural networks based on MobileNet-V2 have an average recognition accuracy of 92.86% for four types of PCB defects, which is better than the ResNet-50 and Vgg-16 networks models, verifying the effectiveness of the PCB defect recognition classification. Moreover, experiments are carried out in a real environment to verify the feasibility of the proposed method.

A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection

Object detection is a well-known task in the field of computer vision, especially the small target detection problem that has aroused great academic attention. In order to improve the detection performance of small objects, in this article, a novel enhanced multiscale feature fusion method is proposed, namely, the atrous spatial pyramid pooling-balanced-feature pyramid network (ABFPN). In particular, the atrous convolution operators with different dilation rates are employed to make full use of context information, where the skip connection is applied to achieve sufficient feature fusions. In addition, there is a balanced module to integrate and enhance features at different levels. The performance of the proposed ABFPN is evaluated on three public benchmark datasets, and experimental results demonstrate that it is a reliable and efficient feature fusion method. Furthermore, in order to validate the applicational potential in small objects, the developed ABFPN is utilized to detect surface tiny defects of the printed circuit board (PCB), which acts as the neck part of an improved PCB defect detection (IPDD) framework. While designing the IPDD, several powerful strategies are also employed to further improve the overall performance, which is evaluated via extensive ablation studies. Experiments on a public PCB defect detection database have demonstrated the superiority of the designed IPDD framework against the other seven state-of-the-art methods, which further validates the practicality of the proposed ABFPN.

LFF-YOLO: A YOLO Algorithm With Lightweight Feature Fusion Network for Multi-Scale Defect Detection

The detection of defects is indispensable in industrial production. Surface defects have different scales. Both minimal flaws and significant scratches may appear on the same product. The standard method uses a multi-scale feature fusion network, introducing many parameters that may reduce the inference speed. In actual industrial production scenarios, inference speed and accuracy play an equally important role. Therefore we propose an algorithm to effectively improve the detection speed while improving the detection accuracy. The model proposed in this paper called “YOLO with lightweight feature fusion network(LFF-YOLO).” First, we use ShuffleNetv2 as a feature extraction network to reduce the number of parameters. Then, to improve the efficiency of multi-scale feature fusion, we propose the lightweight feature pyramid network (LFPN). Considering that the fixed receptive field is difficult to adapt to the defects of different scales, it may lead to the difficulty of model convergence and seriously affect the detection performance. Therefore, we propose the adaptive receptive field feature extraction (ARFFE) module, which weights the multi-receptive field channels to generate multi-receptive field information. In addition, focal loss is used to solve the problem of imbalance between positive and negative samples. Finally, we conducted experiments on NEU-DET (79.23% mAP), Peking University printed circuit board defect dataset (93.31% mAP),and GC10-DET (59.78% mAP), respectively. Extensive experiments show that our proposed method achieves optimal detection speed compared with the prevailing methods, and the detection accuracy of our method is also highly competitive.

Unsupervised Anomaly Detection in Printed Circuit Boards through Student–Teacher Feature Pyramid Matching

Deep learning methods are currently used in industries to improve the efficiency and quality of the product. Detecting defects on printed circuit boards (PCBs) is a challenging task and is usually solved by automated visual inspection, automated optical inspection, manual inspection, and supervised learning methods, such as you only look once (YOLO) of tiny YOLO, YOLOv2, YOLOv3, YOLOv4, and YOLOv5. Previously described methods for defect detection in PCBs require large numbers of labeled images, which is computationally expensive in training and requires a great deal of human effort to label the data. This paper introduces a new unsupervised learning method for the detection of defects in PCB using student–teacher feature pyramid matching as a pre-trained image classification model used to learn the distribution of images without anomalies. Hence, we extracted the knowledge into a student network which had same architecture as the teacher network. This one-step transfer retains key clues as much as possible. In addition, we incorporated a multi-scale feature matching strategy into the framework. A mixture of multi-level knowledge from the features pyramid passes through a better supervision, known as hierarchical feature alignment, which allows the student network to receive it, thereby allowing for the detection of various sizes of anomalies. A scoring function reflects the probability of the occurrence of anomalies. This framework helped us to achieve accurate anomaly detection. Apart from accuracy, its inference speed also reached around 100 frames per second.

Multisensor Image Fusion for Automated Detection of Defects in Printed Circuit Boards

This paper presents multisensor image fusion of polarization and infrared imaging to detect defects in printed circuit boards (PCBs). Many existing automated optical inspection techniques rely on visible imaging sensors. However, collected images suffer from uneven brightness levels due to the influence of lighting environment, which may significantly affect detection accuracies. Polarization information characterizes material types, surface roughness, and geometric shape of an object. Thermal infrared imaging reveals heat radiation difference between the defect region and the background. Polarization and infrared imaging are not sensitive to background illumination and contrast. In this paper, we utilize polarization information as well as infrared imaging to detect the defects in PCBs that conventional optical inspection techniques cannot easily detect. We design a multi-source image acquisition system to simultaneously acquire brightness intensity, polarization, and infrared intensity. Then a Multisensor Lightweight Detection Network (MLDN), trained on the PCB dataset collected, fuses polarization information and the brightness intensities in the visible and thermal infrared spectra to detect defects in challenging lighting conditions. Experiment results show that the proposed network outperforms the state-of-the-art automated optical inspection techniques in terms of mean average precision.

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.