A model-based clustering approach to the recognition of the spatial defect patterns produced during semiconductor fabrication

Yuan, Tao; Kuo, Way

doi:10.1080/07408170701592556

Cited by 42 publications

(8 citation statements)

References 20 publications

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“…Their proposed algorithm was able to identify complicated defect patterns with fewer parameters. Yuan et al in [78], [79] investigated amorphous/linear and curvilinear WM defect patterns simultaneously, and modelled them using multivariate normal distributions and PCs, respectively, extending the traditional modelbased clustering approach by considering the mixture of two different probability densities. However, their approach is mainly based on simulated results and lacks the capability to detect closed-ring shaped patterns that have been widely observed in WM defects.…”

Section: E: Clusteringmentioning

confidence: 99%

“…linear, curvilinear) via various modelbased techniques. The proposed method for classification were compared to the model-based clustering approach used in [78] and it has been found that they were able to detect more clusters for three chosen WM samples. However, in [78] they were able to detect more clusters for one of the samples.…”

Section: E: Clusteringmentioning

confidence: 99%

“…The proposed method for classification were compared to the model-based clustering approach used in [78] and it has been found that they were able to detect more clusters for three chosen WM samples. However, in [78] they were able to detect more clusters for one of the samples. Despite of the good results achieved, their proposed approach faced some limitation such as it uses location information only to analyse defect clusters.…”

Section: E: Clusteringmentioning

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: E: Clusteringmentioning

confidence: 99%

Section: E: Clusteringmentioning

confidence: 99%

Section: E: Clusteringmentioning

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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“…Some statistic-based methods are also applied to wafer defect detection. Hwang and Kuob [5], Yuan and Kuo [6] and Wang et al [7] proposed different probabilistic models to describe wafer defect patterns respectively. Yuan et al [8] proposed a particle filter re-detection method via correlation filters and Tsai and Luo [9] proposed a mean shift-based method for solar wafer surface defect detection.…”

Section: Introductionmentioning

confidence: 99%

A Method for Wafer Defect Detection Using Spatial Feature Points Guided Affine Iterative Closest Point Algorithm

Yang

Rong

et al. 2020

IEEE Access

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In integrated circuit manufacturing industry, in order to meet the high demand of electronic products, wafers are designed to be smaller and smaller, which makes automatic wafer defect detection a great challenge. The existing wafer defect detection methods are mainly based on the precise segmentation of one single wafer, which relies on high-cost and complicated hardware instruments. The segmentation performance obtained is unstable because there are too many limitations brought by hardware implementations such as the camera location, the light source location, and the product location. To address this problem, in this paper, we propose a method for wafer defect detection. This novel method includes two phases, namely wafer segmentation and defect detection. In wafer segmentation phase, the target wafer image is segmented based on the affine iterative closest algorithm with spatial feature points guided (AICP-FP). In wafer defect detection phase, with the inherent characteristics of wafers, a simple and effective algorithm based on machine vision is proposed. The simulations demonstrate that, with these two phases, the higher accuracy and higher speed of wafer defect detection can be achieved at the same time. For real industrial system, this novel method can satisfy the real-time detection requirements of automatic production line.INDEX TERMS Wafer segmentation, defect detection, affine iterative closest point, corner point, machine vision.

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“…It is well known that spatial patterns in the distribution of defects on a die can be related to the potential causes of these defects in the manufacturing process. Given the considerable time, effort, and expense of detecting and classifying spatial defect signatures manually, there has been considerable interest over the past decade in automating the process (Chen and Liu [1], Gleason et al [2], Hansen and Thyregod [3], Jun et al [4], Karnowski et al [5], Ken et al [6], Tobin et al [7], Shankar and Zhong [8], Hwang and Kuo [9], Wang et al [10], and Yuan and Kuo [11]). …”

Section: Introductionmentioning

confidence: 99%

Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation

White

Kundu

Mastrangelo

2008

IEEE Trans. Semicond. Manufact.

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The Hough transformation employing a normal line-to-point parameterization is widely applied in digital image processing for feature detection. In this paper, we demonstrate how this same transformation can be adapted to classify defect signatures on semiconductor wafers as an aid to visual defect metrology. Given a rectilinear grid of die centers on a wafer, we demonstrate an efficient and effective procedure for classifying defect clusters composed of lines at angles of 0 , 45 , 90 , and 135 from the horizontal, as well as adjacent compositions of such lines. Included are defect clusters representing stripes, scratches at arbitrary angles, and center and edge defects. The principle advantage of the procedure over current industrial practice is that it can be fully automated to screen wafers for further engineering analysis.

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A model-based clustering approach to the recognition of the spatial defect patterns produced during semiconductor fabrication

Cited by 42 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

A Method for Wafer Defect Detection Using Spatial Feature Points Guided Affine Iterative Closest Point Algorithm

Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation

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