Defect detection in patterned wafers using anisotropic kernels

Zontak, Maria; Cohen, Israel

doi:10.1007/s00138-008-0146-y

Cited by 31 publications

(34 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We denote this method by Aiger. -The Zontak and Cohen [153] non-local self-similar model using the a-contrario detection threshold as specified in Section 3.4. We denote this method by Zontak.…”

Section: Methodsmentioning

confidence: 99%

“…While Aiger and Talbot [3] works well with the color and the shape, it fails to detect the spatial density anomaly. Zontak and Cohen [153] detects well but also lots of false detection. The other methods Grosjean and Moisan [56], Mishne and Cohen [96], Zontak and Cohen [153] and Boracchi et al [9] over-detect the contours of the non anomalous shapes, thus leading to many false positives.…”

Section: Qualitative Evaluationmentioning

confidence: 99%

“…Automatic anomaly detection has high stakes in industry, remote sensing and medicine ( Figure 1). It is crucial to be able to handle automatically massive data to detect for example anomalous masses in mammograms [56,130], chemical targets in multi-spectral and hyper-spectral satellite images [5,40,124,129], sea mines in side-scan sonar images [95], or defects in industrial monitoring applications [138,149,153]. This detection may be done using any imaging device from cameras to scanning electron microscopes [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image Anomalies: A Review and Synthesis of Detection Methods

et al. 2019

View full text Add to dashboard Cite

We review the broad variety of methods that have been proposed for anomaly detection in images. Most methods found in the literature have in mind a particular application. Yet we focus on a classification of the methods based on the structural assumption they make on the "normal" image, assumed to obey a "background model". Five different structural assumptions emerge for the background model. Our analysis leads us to reformulate the best representative algorithms in each class by attaching to them an a-contrario detection that controls the number of false positives and thus deriving a uniform detection scheme for all. By combining the most general structural assumptions expressing the background's normality with the proposed generic statistical detection tool, we end up proposing several generic algorithms that seem to generalize or reconcile most methods. We compare the six best representatives of our proposed classes of algorithms on anomalous images taken from classic papers on the subject, and on a synthetic database. Our conclusion hints that it is Both authors contributed equally to this work. possible to perform automatic anomaly detection on a single image.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Qualitative Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Anomalies: A Review and Synthesis of Detection Methods

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Saliency Detecting irregularities in recurring structures is also used for defect detection on semiconductor wafers [Shankar and Zhong 2005;Zontak and Cohen 2010], target detection in sonar and multispectral images [Mishne and Cohen 2013] and saliency detection [Boiman and Irani 2007;Seo and Milanfar 2009;Goferman et al 2012]. In these works, the main goal is to detect outliers, i.e., structures that significantly differ from their surrounding.…”

Section: Motion Magnificationmentioning

confidence: 99%

Revealing and modifying non-local variations in a single image

Dekel¹,

Michaeli

Irani

et al. 2015

ACM Trans. Graph.

View full text Add to dashboard Cite

(a) Input Image (c) Exaggerated x2 (b) CorrectedFigure 1: An example of using our Non-Local Variations algorithm to automatically detect and visualize small deformations between repeating structures in a single image (a). Our method can be used to identify and correct these variations, thus producing an 'idealized' version of the image (b). Alternatively, our method can be used to exaggerate these variations (c). In the corrected output, the variability in the shape of the corn's kernels is reduced, and the misalignment of rows is corrected. In the exaggerated output, the subtle differences between the kernels and the row misalignment are highlighted. Photo courtesy of Giandomenico Pozz. AbstractWe present an algorithm for automatically detecting and visualizing small non-local variations between repeating structures in a single image. Our method allows to automatically correct these variations, thus producing an 'idealized' version of the image in which the resemblance between recurring structures is stronger. Alternatively, it can be used to magnify these variations, thus producing an exaggerated image which highlights the various variations that are difficult to spot in the input image. We formulate the estimation of deviations from perfect recurrence as a general optimization problem, and demonstrate it in the particular cases of geometric deformations and color variations.

show abstract

“…Calculation of the difference image is very sensitive to image registration between the reference and inspection images, and can affect the performance of reference-based methods. Zontak and Cohen [4], [5], introduced a method which avoids image registration and is robust to pattern variations, based on anisotropic kernel reconstruction of the source image using its reference image. Defect regions are identified since they cannot be properly reconstructed from the reference image.…”

Section: Introductionmentioning

confidence: 99%

Multi-channel wafer defect detection using diffusion maps

Mishne

Cohen

2014

2014 IEEE 28th Convention of Electrical &Amp; Electronics Engineers in Israel (IEEEI)

Self Cite

View full text Add to dashboard Cite

Detection of defects on patterned semiconductor wafers is a critical step in wafer production. Many inspection methods and apparatus have been developed for this purpose. We recently presented an anomaly detection approach based on geometric manifold learning techniques. This approach is data-driven, with the separation of the anomaly from the background arising from the intrinsic geometry of the image, revealed through the use of diffusion maps. In this paper, we extend our algorithm to 3D data in multichannel wafer defect detection. We test our algorithm on a set of semiconductor wafers and demonstrate that our multiscale multi-channel algorithm has superior performance when compared to single-scale and single-channel approaches.

show abstract

Defect detection in patterned wafers using anisotropic kernels

Cited by 31 publications

References 28 publications

Image Anomalies: A Review and Synthesis of Detection Methods

Image Anomalies: A Review and Synthesis of Detection Methods

Revealing and modifying non-local variations in a single image

Multi-channel wafer defect detection using diffusion maps

Contact Info

Product

Resources

About