Printed Circuit Board Surface Research Articles

A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection

In the manufacturing process of printed circuit boards (PCBs), surface defects have a significant negative impact on product quality. Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized defects, this paper proposes a PCB defect detection algorithm based on a novel YOLOv5 multi-scale attention mechanism(EMA) spatial pyramid dilated Convolution (SPD-Conv) (YOLOv5_ES) network improved YOLOv5s framework. Firstly, the detection head is optimized by removing medium and large detection layers, fully leveraging the capability of the small detection head to identify minor target defects. This approach not only improves model accuracy but also achieves lightweighting. Secondly, in order to further reduce the number of parameters and computational costs, the SPD-Conv is introduced to improve the feature extraction capability by reducing information loss. Thirdly, a EMA module is introduced to fuse context information of different scales, enhancing the model’s generalization ability. Compared to the YOLOv5s model, there is a 3.1% improvement in mean average precision (mAP0.5), a 55.8% reduction in model parameters, and a 4.8% reduction in giga floating-point operations per second (GFLOPs). These results demonstrate a significant improvement in both accuracy and model parameter efficiency.

YOLO-HLT: improved lightweight printed circuit board surface defect detection algorithm based on YOLOv5

YOLO-HLT: improved lightweight printed circuit board surface defect detection algorithm based on YOLOv5

Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs

AbstractHigh-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).

A dataset for deep learning based detection of printed circuit board surface defect

Printed circuit board (PCB) may display diverse surface defects in manufacturing. These defects not only influence aesthetics but can also affect the performance of the PCB and potentially damage the entire board. Thus, achieving efficient and highly accurate detection of PCB surface defects is fundamental for quality control in fabrication. The rapidly advancing deep learning (DL) technology holds promising prospects for providing accurate and efficient detection methods for surface defects on PCB. To facilitate DL model training, it is imperative to compile a comprehensive dataset encompassing diverse surface defect types found on PCB at a significant scale. This work categorized PCB surface defects into 9 distinct categories based on factors such as their causes, locations, and morphologies and developed a dataset of PCB surface defect (DsPCBSD+). In DsPCBSD+, a total of 20,276 defects were annotated manually by bounding boxes on the 10,259 images. This openly accessible dataset is aimed accelerating and promoting further researches and advancements in the field of DL-based detection of PCB surface defect.

Local and Global Context-Enhanced Lightweight CenterNet for PCB Surface Defect Detection.

Printed circuit board (PCB) surface defect detection is an essential part of the PCB manufacturing process. Currently, advanced CCD or CMOS sensors can capture high-resolution PCB images. However, the existing computer vision approaches for PCB surface defect detection require high computing effort, leading to insufficient efficiency. To this end, this article proposes a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet) to detect PCB surface defects in real time. Specifically, we propose a two-branch lightweight vision transformer module with local and global attention, named LGT, as a complement to extract high-dimension features and leverage context-aware local enhancement after the backbone network. In the local branch, we utilize coordinate attention to aggregate more powerful features of PCB defects with different shapes. In the global branch, Bi-Level Routing Attention with pooling is used to capture long-distance pixel interactions with limited computational cost. Furthermore, a Path Aggregation Network (PANet) feature fusion structure is incorporated to mitigate the loss of shallow features caused by the increase in model depth. Then, we design a lightweight prediction head by using depthwise separable convolutions, which further compresses the computational complexity and parameters while maintaining the detection capability of the model. In the experiment, the LGCL-CenterNet increased the mAP@0.5 by 2% and 1.4%, respectively, in comparison to CenterNet-ResNet18 and YOLOv8s. Meanwhile, our approach requires fewer model parameters (0.542M) than existing techniques. The results show that the proposed method improves both detection accuracy and inference speed and indicate that the LGCL-CenterNet has better real-time performance and robustness.

Study on biodegradation of polyurethane coating on PCB by Aspergillus brasiliensis in space

Polyurethane (PU) is a commonly used conformal coating for printed circuit boards (PCBs). Filamentous fungi present in the space station environment are capable of degrading PU coatings, thereby posing a threat to the safety of electronic components. Therefore, we conducted a 90-day interaction test between Aspergillus brasiliensis and PU-coated PCBs in the China Space Station (CSS) and a controlled environment on the ground. This research aimed to investigate the alterations in the physiological properties of A. brasiliensis on PCB surfaces and the deterioration behavior of PU coating systems in the space microgravity environment. The results show that many fungal parameters, such as growth, morphology, cell ultrastructure, and low molecular weight organic acids (LMWOA) accumulation showed strong changes after spaceflight compared to ground controls. Distinct corrosion morphological features were found in the internal coatings by FIB/SEM analysis, suggesting the existence of a particular deterioration mechanism in the microgravity environment, which requires further investigation.

Conceptual demonstration of a Lego-like modular fabrication method for an engineering model of a small monopropellant PCB thrusters

We present an innovative modular fabrication method for a green monopropellant thruster, employing printed circuit boards (PCB) as fundamental building blocks for rapid and efficient performance optimization. This PCB-based approach offers superior thermal and structural characteristics to conventional MEMS-based microthrusters. Furthermore, it streamlines mass production and facilitates the swift and reliable assembly of thrusters using well-established PCB surface mount technology. In this study, we present a conceptual demonstration of this pioneering materials and fabrication technique, exemplified by the development of a 200 mN thrust monopropellant thruster utilizing 90 wt% hydrogen peroxide. Subsequently, we conduct propulsion experiments and consistently observe identical patterns in pulse mode firing tests, demonstrating the repeatability of the system. The experimental results, including thrust and C* efficiencies, provide substantial evidence for the validity and feasibility of our proposed approach. The modular fabrication method presented in this study exhibits high potential for mass production of small satellite thrusters, effectively addressing the expanding thruster market.

Preparation and properties of high abrasion resistant superhydrophobic coating on the PCB

Abrasion resistance has become the bottleneck restricting the application of superhydrophobic surface. It is extremely necessary to design superhydrophobic surface with high abrasion resistance in practical application environment. However, the coexistence of superhydrophobic property and abrasion resistance is a difficult challenge to balance. In this study, a superhydrophobic coating with high abrasion resistance was obtained in combination with three gradients of roughness/wettability, physical adhesion and chemical coupling on the printed circuit board (PCB) surface. The realization of high abrasion resistance of the coating mainly depended on controlling the curing time of the adhesive layer and the way of air injection, so that the micro-nano structure could achieve the effect of semi-embedded. The PCB surface still maintained superhydrophobicity with the water contact angle higher than 150° after a series of tests such as 800 cycles of tape peeling, 400 cycles of knife scraping, 2600 cm of sandpaper abrasion, 425 g of falling sand impacting, and 60 min of water impacting, which fully proved the high abrasion resistance of the coating and the strong adhesion between the coating and the substrate. The preparation process is high efficiency and suitable for large area application, which will provide an extremely valuable idea for improving the waterproof of PCB and promote the true applications of superhydrophobic coating in many fields.

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.

Visual Geometry Group Network for Flexible Printed Circuit Board Surface Defect Classification

Visual Geometry Group Network for Flexible Printed Circuit Board Surface Defect Classification

A mechanistic study identifying improved technology critical metal delamination from printed circuit boards at lower power sonications in a deep eutectic solvent

Deep eutectic solvents (DESs) are an emerging class of ionic liquids that offer a solution to reclaiming technology critical metals (TCMs) from electronic waste, with potential for improved life cycle analysis. The high viscosities typical of DESs, however, impose mass transport limitations such that passive TCM removal generally requires immersion over extended durations, in some cases in the order of hours. It is postulated that, through the targeted application of power ultrasound, delamination of key structures in electronic components immersed in DESs can be significantly accelerated, thereby enabling rapid recovery of TCMs. In this paper, we fully characterise cavitation in a Choline Chloride-Ethylene Glycol DES as a function of sonotrode input power, by the acoustic detection of the bubble collapse shockwave content generated during sonications at more than 20 input powers over the available range. This justifies the selection of two powers for a detailed study of ultrasonically enhanced TCM-delamination from printed circuit boards (PCBs). Dual-perspective high-speed imaging is employed, which facilitates simultaneous observation of TCM removal, and the cavitation evolution and interaction with the PCB surface. Bubble jetting is identified as a key contributor to initial pitting of the TCM layers, exposing the larger underlying copper layer, with the contributions of additional inertial cavitation-mediated phenomena such as bubble-collapse shockwaves also demonstrated as important for delamination. Optimal cavitation activity throughout the sonication then promotes etching of the copper base layer of the PCB structure targeted by the DES, liberating the overlaying TCMs in sections as large as 0.79 mm2. We report a thirtyfold improvement in processing time compared to passive delamination, with sonications at the lower power outperforming those at the higher power. The results demonstrate the potential for industrially scalable recovery of TCMs from the growing quantities of global e-waste, using combined power ultrasonics and DESs.

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.

YOLO-MBBi: PCB Surface Defect Detection Method Based on Enhanced YOLOv5

Printed circuit boards (PCBs) are extensively used to assemble electronic equipment. Currently, PCBs are an integral part of almost all electronic products. However, various surface defects can still occur during mass production. An enhanced YOLOv5s network named YOLO-MBBi is proposed to detect surface defects on PCBs to address the shortcomings of the existing PCB surface defect detection methods, such as their low accuracy and poor real-time performance. YOLO-MBBi uses MBConv (mobile inverted residual bottleneck block) modules, CBAM attention, BiFPN, and depth-wise convolutions to substitute layers in the YOLOv5s network and replace the CIoU loss function with the SIoU loss function during training. Two publicly available datasets were selected for this experiment. The experimental results showed that the mAP50 and recall values of YOLO-MBBi were 95.3% and 94.6%, which were 3.6% and 2.6% higher than those of YOLOv5s, respectively, and the FLOPs were 12.8, which was much smaller than YOLOv7’s 103.2. The FPS value reached 48.9. Additionally, after using another dataset, the YOLO-MBBi metrics also achieved satisfactory accuracy and met the needs of industrial production.

Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection

To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms.

PDMS-polyimide transcutaneous blood gas collector with self-folding out-of-plane heater elements

This paper introduces and evaluates a novel, highly scalable fabrication technique for folding flexible printed circuit board (PCB) features into polydimethylsiloxane (PDMS). The technique is then used to create fast and effective skin-heaters in a prototype gas collector for transcutaneous blood gas monitoring (TBM), a well-established technique to non-invasively measure the amount of CO2 and O2 in a patient’s blood. Previous studies have shown that TBM can be made safer by heating the patient’s skin with short pulses rather than continuously. Hence, the effects of incorporating a resistive heater with folded heating elements into a PDMS gas collector was investigated and compared to a heater with surface mounted heating elements. The results show that the fabrication technique provides consistent, controllable folding angles using only the surface and viscous forces of the flexible PCB and PDMS. With the investigated design- and material parameters a maximum folding angle of 30° was achieved, resulting in a 2000% increase in initial surface heating compared to an un-folded reference. For the intended application, this corresponds to reducing the time needed to heat the skin of a patient to less than half. The presented fabrication technique is, however, not limited to the application investigated in this paper, but rather offers the possibility to quickly and automatically fold complex structures and circuits into the bulk of the PDMS without introducing any time overhead as the number of features and folds grow.

FM-STDNet: High-Speed Detector for Fast-Moving Small Targets Based on Deep First-Order Network Architecture

Identifying objects of interest from digital vision signals is a core task of intelligent systems. However, fast and accurate identification of small moving targets in real-time has become a bottleneck in the field of target detection. In this paper, the problem of real-time detection of the fast-moving printed circuit board (PCB) tiny targets is investigated. This task is very challenging because PCB defects are usually small compared to the whole PCB board, and due to the pursuit of production efficiency, the actual production PCB moving speed is usually very fast, which puts higher requirements on the real-time of intelligent systems. To this end, a new model of FM-STDNet (Fast Moving Small Target Detection Network) is proposed based on the well-known deep learning detector YOLO (You Only Look Once) series model. First, based on the SPPNet (Spatial Pyramid Pooling Networks) network, a new SPPFCSP (Spatial Pyramid Pooling Fast Cross Stage Partial Network) spatial pyramid pooling module is designed to adapt to the extraction of different scale size features of different size input images, which helps retain the high semantic information of smaller features; then, the anchor-free mode is introduced to directly classify the regression prediction information and do the structural reparameterization construction to design a new high-speed prediction head RepHead to further improve the operation speed of the detector. The experimental results show that the proposed detector achieves 99.87% detection accuracy at the fastest speed compared to state-of-the-art depth detectors such as YOLOv3, Faster R-CNN, and TDD-Net in the fast-moving PCB surface defect detection task. The new model of FM-STDNet provides an effective reference for the fast-moving small target detection task.

PCB-YOLO: An Improved Detection Algorithm of PCB Surface Defects Based on YOLOv5

To address the problems of low network accuracy, slow speed, and a large number of model parameters in printed circuit board (PCB) defect detection, an improved detection algorithm of PCB surface defects based on YOLOv5 is proposed, named PCB-YOLO, in this paper. Based on the K-means++ algorithm, more suitable anchors for the dataset are obtained, and a small target detection layer is added to make the PCB-YOLO pay attention to more small target information. Swin transformer is embedded into the backbone network, and a united attention mechanism is constructed to reduce the interference between the background and defects in the image, and the analysis ability of the network is improved. Model volume compression is achieved by introducing depth-wise separable convolution. The EIoU loss function is used to optimize the regression process of the prediction frame and detection frame, which enhances the localization ability of small targets. The experimental results show that PCB-YOLO achieves a satisfactory balance between performance and consumption, reaching 95.97% mAP at 92.5 FPS, which is more accurate and faster than many other algorithms for real-time and high-precision detection of product surface defects.

Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

Industry 4.0 is currently the goal of many factories, promoting manufacturing factories and sustainable operation. Automated Optical Inspection (AOI) is a part of automation. Products in the production line are usually inspected visually by operators. Due to human fatigue and inconsistent standards, product inspections still have defects. In this study, the sample component assembly printed circuit board (PCB), PCB provided by the company was tested for surface components. The types of defects on the surface of the PCB include missing parts, multiple parts, and wrong parts. At present, the company is still using visual inspection by operators, the PCB surface components are more complex. In order to reduce labor costs and save the development time required for different printed circuit boards. In the proposed method, we use digital image processing, positioning correction algorithm, and deep learning YOLO for identification, and use 450 images and 10500 components of the PCB samples. The result and contribution of this paper shows the total image recognition rate is 92% and the total component recognition rate reaches 99%, and they are effective. It could use on PCB for different light, different color backplanes, and different material numbers, and the detection compatibility reaches 98%.

The effect of quaternary ammonium salts with different chain lengths on copper filling behavior in blind holes of printed circuit board

The rapid and defect free blind hole copper super filling is the key technology to ensure the quality and development of printed circuit boards (PCBs). The linear quaternary ammonium compounds with a low molecular weight are expected to act as a potential suppressor for blind hole copper filling. Here, three quaternary ammonium salts with different molecular chain lengths (the dodecyl trimethyl ammonium chloride, the cetyl trimethyl ammonium chloride and the stearyl trimethyl ammonium chloride) were selected to study the effects of the molecular chain length of quaternary ammonium salt on the copper filling behavior of the PCB blind holes. The electrochemical results show that the three quaternary ammonium salts show greater inhibition ability on copper deposition compared with the traditional additives (polyethylene glycol (PEG) and Cl−) and furthermore, the suppressor effect becomes stronger with the increase of molecular weight. The blind hole copper filling experiment of PCB is also carried out to study the copper filling capacity of three quaternary ammonium salts. The filling experiment reveals that compared with the traditional suppressors (PEG and Cl−), the filling quality of blind hole copper is significantly improved when the three quaternary ammonium salts are used as suppressors. Moreover, under the coordination of accelerator (bis (3-sulfopropyl) disulfide (SPS)), the three quaternary ammonium salts can realize more rapid copper super filling without chloride ion in the electrolyte. Cross-section and surface morphological analysis further illuminates that the quaternary ammonium salt as an suppressor can also more effectively inhibit the deposition of copper on the PCB surface and reduce the roughness of the copper layer. The present results imply that quaternary ammonium salts have greater advantages for copper filling and are expected to replace traditional suppressors as new suppressors.

A Lightweight Modified YOLOX Network Using Coordinate Attention Mechanism for PCB Surface Defect Detection

Surface defect detection for the printed circuit board (PCB) is essential in PCB manufacturing. Existing defect detection networks have several problems: low detection efficiency, high memory consumption, and low sensitivity to small defects. To address these issues, we propose a new lightweight deep-learning-based defect detection network, YOLOX with a modified CSPDarknet and coordinate attention (YOLOX-MC-CA). YOLOX-MC-CA is developed on the YOLOX and uses the coordinate attention (CA) mechanism to improve the recognition capability of small PCB surface defects. The backbone network in YOLOX is also modified into a new CSPdarknet structure with some inverted residual blocks. The modified CSPDarknet (MC) backbone network helps the YOLOX decrease the number of parameters on the premise of guaranteeing the feature extraction ability. We evaluated the YOLOX-MC-CA with an augmented dataset based on a public PCB surface defect dataset. Compared to the squeeze-and-excitation (SE) module, convolutional block attention module (CBAM), and other approaches in previous research, the CA mechanism improves the network with more detection precision for the small PCB surface defects. The experimental results demonstrate that our network is superior to other state-of-the-art (SOTA) networks for PCB surface defect detection, scoring 99.13% on mean average precision (mAP) and 47.6 frames per second (FPS) on detection speed, only occupying a parameter space of 3.79 million (M). It demonstrates that the proposed network is more suitable for deployment on embedded systems.