An Automatic Method for Extracting and Classifying Defect in Optical Photomask Images

Ha, Youngmin; Jeong, Hong

doi:10.1109/mue.2007.66

Cited by 5 publications

(5 citation statements)

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“…4e). [1,2] used subtracted image between reference and test image for detecting obscure defects. Pixel values in subtracted image have narrow range of intensity values from 0 to 30 over the entire image (Fig.…”

Section: ) Small and Obscure Defectmentioning

confidence: 99%

“…Although the difference based methods only consider corresponding points in each pixel [1,2], the HOM considers corresponding points with neighboring pixels as well.…”

Section: ) Small and Obscure Defectmentioning

confidence: 99%

“…If we can accurately identify and classify photomask defects, we can determine whether the photomask patterns need repair or modification. If foreign particles are evident on glass or chrome, the photomask should be washed, and scrutinized for other defects such as a bridges or coupling patterns [1].…”

Section: Introductionmentioning

confidence: 99%

“…Yamanaka et al [3] used a function to extract edges from a mask SEM image. Ha and Jeong [1,2], propose an automatic method for defect extraction based on determining absolute differences between reference and test images. Ha and Jeong [1] classified defects into two groups, apparent and obscure, and utilized two steps for defect extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Ha and Jeong [1,2], propose an automatic method for defect extraction based on determining absolute differences between reference and test images. Ha and Jeong [1] classified defects into two groups, apparent and obscure, and utilized two steps for defect extraction. The first step is binarization (segmentation) of photomask images in order to identify pattern and background.…”

Section: Introductionmentioning

confidence: 99%

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Defect Extraction in Photomask Image Using High Order Moment

Choi

Jeong

2009

2009 Fourth International Conference on Computer Sciences and Convergence Information Technology

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This paper describes an automated technique to extract defects in photomask images. Conventional approaches to photomask inspection rely on difference based techniques which are susceptible to distortion from image 'noise'. We propose a robust method based on high order moment (HOM) between reference and test images. In comparison to difference based methods, HOM reveals a wide distribution range of pixel values. This result implies that we can specify pixel value thresholds, and use these thresholds as a basis for distinguishing defects from the background. Our proposed algorithm guarantees the accurate extraction of defects. In this paper, we compare and contrast the transmitted reference image with the test image and reflected image pairs.

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Section: ) Small and Obscure Defectmentioning

confidence: 99%

“…Although the difference based methods only consider corresponding points in each pixel [1,2], the HOM considers corresponding points with neighboring pixels as well.…”

Section: ) Small and Obscure Defectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%