A novel defect detection and identification method in optical inspection

Xie, Liangjun; Huang, Rui; Gu, Nong; Cao, Zhiqiang

doi:10.1007/s00521-013-1442-7

Cited by 32 publications

(22 citation statements)

References 23 publications

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“…Common internal defect detection technologies of the workpiece include ultrasonic, laser holography, X-ray photography, etc. Industrial CT images are currently the most effective non-destructive testing technology [17], making it easier to identify defects. Defect recognition based on industrial CT images is a simple and efficient method.…”

Section: Discussionmentioning

confidence: 99%

Defect features recognition in 3D industrial CT images

Jiang¹

2018

IJCAI

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Due to the limitations of production conditions, there is a certain probability that workpiece product has internal defects, which will have a certain impact on the performance of workpiece. Therefore, the internal defects detection of workpiece is essential. This study proposed a defect recognition method based on industrial computed tomography (CT) image to identify the internal defects of workpiece. The block fractal algorithm was used to locate the defect parts of the image, then the improved k-means clustering algorithm was used to segment the defect parts, and feature vector was extracted by Hu invariant moments. Finally, the firefly algorithm and radial basis function (RBF) neural network were combined to identify the defect. It was found from the experiments that the algorithm in this study had the accuracy of 97.89%, which proved the reliability of the algorithm and provided some suggestions for the defects recognition. Povzetek: Za prepoznavanje okvar na 3D slikah industrijskih izdelkov je uporabljena metoda vinske mušice.

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Section: Discussionmentioning

confidence: 99%

Defect features recognition in 3D industrial CT images

Jiang¹

2018

IJCAI

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“…DeNicolao et al 16 simulated binary failure data on wafers assuming that the failure probability has relationships with the center of failure, and this method was also used by Jeong et al 20 and Xie et al 36 . However, this method directly generated the probability of failure according to physical locations and did not explicitly separate clustered defects and random defects.…”

Section: Generation Of Simulated Datamentioning

confidence: 99%

Wafer yield prediction using derived spatial variables

Dong

Chen

Wang

2017

Quality & Reliability Eng

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Unreliable chips tend to form spatial clusters on semiconductor wafers. The spatial patterns of these defects are largely reflected in functional testing results. However, the spatial cluster information of unreliable chips has not been fully used to predict the performance in field use in the literature. This paper proposes a novel wafer yield prediction model that incorporates the spatial clustering information in functional testing. Fused LASSO is first adopted to derive variables based on the spatial distribution of defect clusters. Then, a logistic regression model is used to predict the final yield (ratio of chips that remain functional until expected lifetime) with derived spatial covariates and functional testing values. The proposed model is evaluated both on real production wafers and in an extensive simulation study. The results show that by explicitly considering the characteristics of defect clusters, our proposed model provides improved performance compared to existing methods. Moreover, the cross‐validation experiments prove that our approach is capable of using historical data to predict yield on newly produced wafers.

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“…Automated optical inspection (AOI) equipment has been widely used for real-time detection of defects and quality control of products on the production line. [1][2][3] Artificial neural networks have also been applied to these AOI systems to improve the accuracy of defect inspection. [4][5][6] Although the introduction of artificial intelligence (AI) enhances the speed and accuracy for defect detection, an effective advice for evaluation and improvement of the manufacturing process from the inspected results is still a problem until now.…”

Section: Introductionmentioning

confidence: 99%

Intelligent defect classification system based on deep learning

Pan

Chang

et al. 2018

Advances in Mechanical Engineering

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A fast and intelligent defect classification system for distinguishing defect features is developed in this study. Defect images obtained from an automated optical inspection instrument are first trained utilizing a deep learning approach based on the convolutional neural network. The detailed features of defects, such as flaws in the inclination, size, quantity, and settlement, can then be characterized with the developed system. The obtained defect characteristics can be provided as a reference to evaluate the manufacturing process. A graphics processing unit card is used to build the parallel computing architecture for fast data computation both in the training and classification processes. The experimental results show that the defect classification for a touch panel glass surface of size 43 3 229.4 mm 2 that yields 805 million pixel data points was completed in 2 s. The classification accuracy was above 96%. Therefore, an automated and fast defect analysis catalog integrated to an optical inspection result for the evaluation and adjustment of manufacturing operations can be expected with the proposed approach.

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A novel defect detection and identification method in optical inspection

Cited by 32 publications

References 23 publications

Defect features recognition in 3D industrial CT images

Defect features recognition in 3D industrial CT images

Wafer yield prediction using derived spatial variables

Intelligent defect classification system based on deep learning

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