Performance evaluation of wavelet-based PCB defect detection and localization algorithm

MIT Lincoln Laboratory is working towards the development of a tileable radar panel to satisfy multimission needs. A combination of custom and commercial off-the-shelf (COTS) Monolithic Microwave Integrated Circuits (MMICs) have been developed and/or employed to achieve the required system functionality. The integrated circuits (ICs) are integrated into a low cost T/R module compatible with commercial printed circuit board (PCB) manufacturing. Sixty-four of the transmit/receive (T/R) modules are integrated onto the aperture PCB in an 8 × 8 lattice. In addition to the T/R elements, the aperture PCB incorporates transmit and receive beamformers, power and logic distribution, and radiating elements. The aperture PCB is coupled with a backplane PCB to form a panel, the line replaceable unit (LRU) for the multifunction phased array radar (MPAR) initiative. This report summarizes the evaluation of the second iteration LRU aperture PCB and T/R element. Support fixturing was developed and paired with the panel to enable backplane functionality sufficient to support the test objectives.

The research object in the work is the printed circuit boards (PCB) Production technological process using the additive technology of photopolymer 3D printing. The existing problem is that the manufacturing process of single-sided and double-sided PCBs, simple in technology, from the third to the fifth accuracy class, requires the use of a large amount of consumables and technological equipment. In turn, this affects the cost of the product. The research subject is models and methods for manufacturing PCB using photopolymer 3D printing. In order to reduce the cost of materials: film or aerosol photoresist, as well as reduce the number of technological operations, applying photoresist and for the manufacture of PCBs stencils, it is proposed to use photopolymer 3D printing technologies for the manufacture of PCBs. The paper analyzes the costs of Plexiwire Resin Basic Orange Transparen photopolymer resin for the manufacture of single-sided PCBs and calculates the cost of the consumable (resin) compared to the costs of dry film photoresist. 60 % cost of consumables (photopolymer resin) compared to dry film photoresist for making single-sided PCBs. The work is aimed at determining the dependence of the geometric dimensions of the PСВs topology and the consumption of photopolymer resin on the technological parameters of photopolymer exposure. A regression correlation model of the dependence of resin consumption on exposure parameters has been developed and correlation coefficients have been calculated. It has been established that with an increase in the exposure time of the photopolymer resin, the consumption of the photopolymer resin increases and the deviation of the geometric dimensions of the PCBs topology increases, which in turn negatively affects the quality of the product. Therefore, using the obtained regression model, it is possible to calculate the influence of parameters on the PCB topology and reduce the deviation of conductor sizes and resin consumption.

Printed circuit boards (PCBs) are primarily used to connect electronic components to each other. It is one of the most important stages in the manufacturing of electronic products. A small defect in the PCB can make the final product inoperable. Therefore, careful and meticulous defect detection steps are necessary and indispensable in the PCB manufacturing process. The detection methods can generally be divided into manual inspection and automatic optical inspection (AOI). The main disadvantage of manual detection is that the detection speed is too slow, resulting in a waste of human resources and costs. Thus, in order to speed up the production speed, AOI techniques have been adopted by many PCB manufacturers. Most current AOI mechanisms use traditional optical algorithms. These algorithms can easily lead to misjudgments due to different light and shadow changes caused by slight differences in PCB placement or solder amount so that qualified PCBs are judged as defective products, which is also the main reason for the high misjudgment rate of AOI detection. In order to effectively solve the problem of AOI misjudgment, manual re-judgment is currently the reinspection method adopted by most PCB manufacturers for defective products judged by AOI. Undoubtedly, the need for inspectors is another kind of labor cost. To reduce the labor cost. of manual re-judgement, an accurate and efficient PCB defect reinspection mechanism based on deep learning algorithm is proposed. This mechanism mainly establishes two detection models, which can classify the defects of the product. When both models have basic recognition capabilities, the two models are then combined into a main model to improve the accuracy of defect detection. In the study, the data provided by Lite-On Technology Co., Ltd. were implemented. To achieve the practical application value in the industry, this research not only considers the problem of detection accuracy, but also considers the problem of detection execution speed. Therefore, fewer parameters are used in the construction of the model. The research results show that the accuracy rate of defect detection is about 95%, and the recall rate is 94%. Compared with other detection modules, the execution speed is greatly improved. The detection time of each image is only 0.027 s, which fully meets the purpose of industrial practical application.

Electronic components (EC) detection is relevant for Printed Circuit Board (PCB) manufacturing, quality inspection, and assurance (cyber-security). Moreover, at end-of-life electronic devices, the PCBs become Waste PCBs (WPCBs), the primary source of high-value materials in electronic waste. However, the recycling process is challenging because of the WPCB’s high composition diversity. EC detection in WPCBs can reduce the uncertainty about WPCBs composition and help select a better recycling process. Nevertheless, large fully-annotated datasets for the PCB domain are available at a high cost. For WPCBs, there is only one publicly available dataset, the PCB-DSLR, and it is partially labeled. As for PCBs, the FICS-PCB dataset is available. However, it contains images of modern and clean PCBs, all in good condition, while the first has broken and dirty parts. Thus, we propose a self-training strategy for object detection algorithms without needing a fully labeled dataset. In the strategy, the YOLOv5 model is used to detect the eight types of electronic components present in FICS-PCB in PCB-DSLR, which initially has labels only for integrated circuits. The process occurs through the interaction between a teacher model, trained with the FICSPCB data, and a student model, which is trained by joining the FICS-PCB data and the teacher-generated labels for PCB-DSLR. The results show that the student model performs better in the electronic components detection task, obtaining greater precision and sensitivity when compared to the teacher model. Since we are dealing with partially labeled data, we provide a low-dimensional representation of the detections of both models using t-SNE.

This paper presents a Charged System Search (CSS) algorithm to solve for robotic drill path optimization involved in printed circuit board (PCB) manufacturing industries. Most of the operational time of a PCB Robotic Drill is spent on moving the drill bit between the holes. An optimized path translates to a minimal cost of operating the robot. The drill path consists of a number of potential locations where the holes are going to be drilled. As the number of holes required increases so thus does the complexity to find the optimized path. Recently developed CSS algorithm proposed to solve this complex problem with minimal computational time. The performance of CSS algorithm is tested and verified with four case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable to efficiently find the optimal path for PCB holes drilling process with reasonable computational time.

There are many complex problems in the optimization of an electronics manufacturing environment, and it is the view of the authors that these problems should not be solved and optimized in isolation, but analysed in the framework of a system. A systems approach offers an overall approach for solving problems, and optimizing the whole of the system as well as discrete subsystems. The research introduced in this paper integrates several techniques, namely: Integrated computer aided manufacturing DEFinition (IDEF), and experimental design and response surface methods for the analysis, control and optimization of electronic manufacturing processes. Electronics manufacturing includes three major processes; Printed Circuit Board (PCB) manufacturing, semiconductor device manufacturing and electronics assembly. This paper describes a novel generic systematic methodology that has been used to create a model to optimize the photolithography process in PCB manufacture. For this, photolithography has been considered as a whole system made up of several sub-systems. This is shown in the process map for PCBs that focuses on photolithography and its subprocesses. A model of the manufacturing process is then given with the results of this being validated using an industrial study. Optimized settings for processing equipment are given resulting in an increase in process yield within industry.

With lead-free legislation impacting printed circuit board (PCB) manufacturing, immersion silver (ImAg) finish is adapted as the top finish of copper trace on PCB due to its many advantages. However, it is easy for silver to form electrochemical migration (ECM) in high humid environment, so that the potential bias and spacing between adjacent circuits are limited. In addition, since the thickness of ImAg finish is usually less than 0.15µm restrained by the electroless plating process, the copper trace under the silver finish cannot be fully covered, which causes the complexity of corrosion and electrochemical migration. In this paper, water drop test was used to induce the electrochemical migration on ImAg finished “Y” pattern circuits on PCB. The surface insulation resistance was detected by a picoammeter and the growth of dendrite deposit between two electrodes was monitored by a video system. The electrolytic reaction and galvanic reaction on the electrodes formed by bimetal couples was analyzed theoretically. The mechanism of electrochemical migration of copper driven by immersion silver finish was discussed. The effects of potential bias, spacing and electric field intensity between adjacent circuits on electrochemical migration were discussed.

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.

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Waste minimization and its ecological evaluation A case study in printed circuit board manufacture

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%.

PurposeTo present and discuss open trace defects uncovered in an FR4 assembly during electrical testing.Design/methodology/approachThis paper presents open trace defects observed in FR4 assemblies and analyses the distribution of defects. The paper also discusses possible root causes for their occurrence.FindingsThe open trace defects that occurred during printed circuit board (PCB) fabrication should have been observed by the board manufacturer. It appears that the PCB manufacturer did not perform automatic optical inspection (AOI) and electrical testing during the manufacturing of the boards. The cost due to the rejected PCBAs was approximately 3x times that of the PCB cost.Originality/valueThe paper highlights the costly impact of uncovering a PCB defect after assembly. Based on the results of this study, the implementation of electrical testing and AOI for PCBs is recommended.

Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PDF

On the selection of a printed circuit board assembly line system

The number of printed circuit board (PCB) manufacturers in the UK fell by 60% over the period 1990 to July 1993, according to a new report ‘UK PCB industry 1990–1993’ recently published by the PCIF.