Improved Pattern Matching Applied to Surface Mounting Devices Components Localization on Automated Optical Inspection

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.

Automatic optical inspection (AOI) of micro drill bit becomes more and more important with the rapid expanding of printed circuit board (PCB) manufacturing industry. Distinguished from most traditional manual inspection approach, AOI is time-saving, objective and non contact. In this work, a pattern classification method is proposed for the AOI of micro drill bit in PCB manufacturing, in which three features of drill bit blade are extracted for classification. In order to be independent on the clamp that can guarantee the exact position of drill bit blade for photography, and reduce the cost of the AOI system, an image registration method is used to align the drill bit blade, which can also make the feature extraction much easier. The evaluation result indicates that the approach works well for the AOI of micro drill bit. It is real time, more detailed result providing and low requirement on photographic device.

An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

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.

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.

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

In computer vision, shape recovery is a classic problem. The shape from shading problem aims to reconstruct a 3D shape from one or more intensity images. Inspection of solder joint is a critical step in the assembly of PCB (Printed Circuit Board). So, non-destructive inspection technology has been widely used in a production of PCB. In automated optical inspection (AOI) field 2D inspection appears to be more prevalent industry-wide than 3D inspection, but the limit of 2D inspection in some defects is apparent. This paper deals with the reconstruction of the solder joint's surface in PCB based on shape form shading technology. The advantages of SFS method are analyzed firstly. Then, theory and survey of SFS method is introduced. To overcome the disadvantages existing in conventional SFS methods, some improved algorithms are provided and proved by experiments. Experimental results reveal that the proposed method shows practical value in solder joint inspection.

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.

Circuit boards are one of the key elements of the electronics industry. Because of its demand for portable electronic goods, the production of circuit boards has become more significant. The most significant aspect of printed circuit board production is AOI or Automatic Optical Inspection. When generated in a single quantity, the PCB requires a small way to teach and change the AOI method for process validation. This paper proposes a mechanism of detection of capacitors trained on circuit boards using the YOLO V3 algorithm. YOLO is a form of rapid object detection based on the convolutional neural network or CNN. CNN’s deep network can distinguish specific characteristics from all the image features. The study developed an AI with the same feature that the manufacturing industry uses to assist students with an effective evaluation of their circuit boards. It focuses on the capacitor in a circuit board. It will not, however, be capable of distinguishing other forms of electronic components. Nearly 60 percent of the entire test has a performance of 100 percent detection, according to the test results, while 33.33 percent is over 70 percent detection and 6.66 percent has a 0 percent detection output. Capacitor recognition has been done with an overall testing accuracy of 93.33%.

Recently, there has been a growing focus in solving approximate pattern matching problems in the streaming model. Of particular interest are the pattern matching with k-mismatches (KMM) problem and the pattern matching with w-wildcards (PMWC) problem. Motivated by reductions from these problems in the streaming model to the dictionary matching problem, this paper focuses on designing algorithms for the dictionary matching problem in the multi-stream model where there are several independent streams of data (as opposed to just one in the streaming model), and the memory complexity of an algorithm is expressed using two quantities: (1) a read-only shared memory storage area which is shared among all the streams, and (2) local stream memory that each stream stores separately. In the dictionary matching problem in the multi-stream model the goal is to preprocess a dictionary D={P_1,P_2,...,P_d} of d=|D| patterns (strings with maximum length m over alphabet Sigma) into a data structure stored in shared memory, so that given multiple independent streaming texts (where characters arrive one at a time) the algorithm reports occurrences of patterns from D in each one of the texts as soon as they appear. We design two efficient algorithms for the dictionary matching problem in the multi-stream model. The first algorithm works when all the patterns in D have the same length m and costs O(d log m) words in shared memory, O(log m log d) words in stream memory, and O(log m) time per character. The second algorithm works for general D, but the time cost per character becomes O(log m+log d log log d). We also demonstrate the usefulness of our first algorithm in solving both the KMM problem and PMWC problem in the streaming model. In particular, we obtain the first almost optimal (up to poly-log factors) algorithm for the PMWC problem in the streaming model. We also design a new algorithm for the KMM problem in the streaming model that, up to poly-log factors, has the same bounds as the most recent results that use different techniques. Moreover, for most inputs, our algorithm for KMM is significantly faster on average.

A popular soldering technique for printed circuit boards (PCB) is the so-called surface-mounted technology. After the soldering process an automated optical inspection (AOI) is the common method determining whether a PCB shall go to a manual inspection and rework station (MIS) or can directly go further to the next process step. Thereby, the AOI is a vision-based system deriving user defined physical measurements from a camera image. Based on these pre-defined measurements associated with static specification limits, the AOI labels each inspected soldering spot on a PCB as non-defect or defect. However, a large majority of PCBs are wrongly labelled defect, so-called false calls, causing a major manual labour effort at the MIS.This dataset contains a 132-days recording of PCBs going through the MIS labelled as true defect or false call with the physical measurement by the AOI. Furthermore, the dataset may contain various distribution drifts of unknown type that can be explained by the high sensitivity of electronic production to small external factors that may change unrecognized and additionally the dataset has an unknown percentage of label error due the human labelling process.

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.

In this paper, an improved efficient Faster R-CNN algorithm used in PCB solder joint defects and components detection is proposed. The accurate detection of solder joint defects and components on printed circuit boards (PCB) is very important to the quality of production of electronic products. The traditional automatic optical inspection needs a customized registration and program design for each circuit board. The necessary steps are complex and time-consuming, and the judgment standard is relatively simple and rigid, resulting in a poor generalization ability of detection and decreased detection accuracy, especially in the conditions of poor lighting, color change of circuit board, and multiple-variety and mini-batch production scenarios. In this paper, an improved Faster R-CNN PCB assembly solder joint defects and components detection algorithm, named Efficient Faster R-CNN, is proposed based on EfficientNet-B7. In this algorithm, the first eight stages of EfficientNet-B7 network structure are used to replace VGG-16 for feature extraction, and the generalized intersection over union ratio is used as the loss of bounding-box regression to avoid the non-optimal solution caused when the prediction boxes and the real boxes are disjoint. The proposed algorithm also uses the swish activation function with better performance to replace ReLU.The experimental results show that the detection accuracy of the proposed algorithm is significantly improved, with the mean average precision close to 0.99, which is of great significance for industrial application.

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.

Nowadays signature attacks are termed as very big problem because it leads to software vulnerability.Malware writers confuse their malicious code to malicious code detectors such as Signature-based detection. However,it fails to detect new malware. This research article addresses the signature based intrusion detection from IntrusionDetection (IDS) systems. The proposed hybrid techniques for Generation of Signature are done using GeneticAlgorithm (GA) and Simulated Annealing (SA) approaches. For this, signature-set in execution statements are selected by using simulated annealing and genetic algorithm, which produce the optimal solution of selection. Then thegenerated signatures are matched with IDS by using the two pattern matching techniques, namely (i). Finite stateautomaton based search for Single Pattern matching technique and (ii) Rabin Karp string search algorithm for multiple pattern matching technique. These techniques are used to match the signature as in an effective manner. In addition tothis the Fuzzy Logic classification is used to find the degrees of truth of vulnerability for classification. The aim ofthe proposed work is to improve the final resultant accuracy in compared to existing techniques. The proposed RabinKarp- fuzzy logic system returns the higher performance metrics namely precision is 88% and Recall is 80% and inopen source dataset it contains 30 vulnerabilities this proposed worked well in detecting 28 vulnerabilities/ defect, theaccuracy of this proposed is 94.27%.