Improved Normalized Cross-Correlation for Defect Detection in Printed-Circuit Boards

Annaby, M. H.; Fouda, Y. M.; Rushdi, Muhammad A.

doi:10.1109/tsm.2019.2911062

Cited by 68 publications

(29 citation statements)

References 20 publications

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“…The main steps in the manufacturing of PCB are the following: applying solder paste to PCB, placing IC on the board at correct positions and placing the board in an oven to solder components to pads. [137]. During the manufacturing of PCBs, various defects may occur at each stage, most of them occur at soldering and component placement stages as shown in the pie chart of Figure 11.…”

Section: Pcb Defectsmentioning

confidence: 99%

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: Pcb Defectsmentioning

confidence: 99%

“…Missing Component [137], [139], [163], [179]- [181] Wrong Component [139], [171], [172], [180] Shifted or rotated component [139], [159], [160], [163], [171], [172], [179], [180] Component lifted (tombstone) [139], [159], [163], [171], [172], [180] Component misplacement [182]- [186] IC molding surface…”

Section: Componentsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…Normalized cross-correlation (NCC) is an important mathematical tool in signal and image processing for feature matching, similarity analysis, motion tracking, object recognition, and so on [1][2][3]. In order to improve its real-time and efficient performance, digital NCC has been suggested to be implemented by some fast algorithms and hardware structures, due to its high computational complexity [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, since correlation and convolution have similar computation structures, there are mainly three kinds of fast convolution algorithms can be applied for fast NCC [6,7]: (1) the Fast Fourier Transform (FFT)-based algorithm, (2) the polynomial-based algorithm, (3) the decomposition algorithm. However, to our knowledge, each of these algorithms has its applicable limitations.…”

Section: Introductionmentioning

confidence: 99%

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

Pan

Xia

et al. 2020

Sensors

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Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.

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“…Surface defect detection and classification are important tasks in many fields, such as the manufacturing industry [1,2], textile industry [3], and printing industry [4,5]. For the printing industry, with the increasing speed of modern printing machines and the increasing requirements of printing enterprises in terms of product quality, traditional detection and classification methods based on human visual inspection are impossible.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

Zhang

et al. 2019

Symmetry

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Deep learning has been successfully applied to classification tasks in many fields due to its good performance in learning discriminative features. However, the application of deep learning to printing defect classification is very rare, and there is almost no research on the classification method for printing defects with imbalanced samples. In this paper, we present a deep convolutional neural network model to extract deep features directly from printed image defects. Furthermore, considering the asymmetry in the number of different types of defect samples-that is, the number of different kinds of defect samples is unbalanced-seven types of over-sampling methods were investigated to determine the best method. To verify the practical applications of the proposed deep model and the effectiveness of the extracted features, a large dataset of printing detect samples was built. All samples were collected from practical printing products in the factory. The dataset includes a coarse-grained dataset with four types of printing samples and a fine-grained dataset with eleven types of printing samples. The experimental results show that the proposed deep model achieves a 96.86% classification accuracy rate on the coarse-grained dataset without adopting over-sampling, which is the highest accuracy compared to the well-known deep models based on transfer learning. Moreover, by adopting the proposed deep model combined with the SVM-SMOTE over-sampling method, the accuracy rate is improved by more than 20% in the fine-grained dataset compared to the method without over-sampling.

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Improved Normalized Cross-Correlation for Defect Detection in Printed-Circuit Boards

Cited by 68 publications

References 20 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

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