&lt;title&gt;Machine vision algorithms for semiconductor wafer inspection: a project with Inspex&lt;/title&gt;

Chen, Chi Hau; Cheng, Tzu-Hung; Wu, Wo-Tak; Driscoll, Shawn P.

doi:10.1117/12.326963

Cited by 7 publications

(4 citation statements)

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“…This method is very fast but a major disadvantage is that small defects cannot be recognised. Wavelet technique (Chen et al 1998) has also been involved in wafer inspection. A fairly complete review of the related literature may be found in Babian (1986), Newman et al (1995), Moganti et al (1996Moganti et al ( , 1998aMoganti et al ( , 1998b).…”

Section: Backgroundsmentioning

confidence: 99%

A golden-template self-generating method for patterned wafer inspection

Xie

Guan

2000

Machine Vision and Applications

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This paper presents a novel golden template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both directions. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find out possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented.

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Section: Backgroundsmentioning

confidence: 99%

A golden-template self-generating method for patterned wafer inspection

Xie

Guan

2000

Machine Vision and Applications

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“…This method is very fast, but a major disadvantage is that small defects cannot be recognised. Wavelet technique [17] has also been used in wafer inspection. A complete review of the related literature may be found in Babian [2], Newman et al [11], Moganti et al [12], [15] and [16].…”

Section: Introductionmentioning

confidence: 99%

Image Wafer Inspection based on Template Matching

Barone¹

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper presents a template matching technique for detecting defects in VLSI wafer images. This method is based on traditional techniques of image analysis and image registration, but it combines the prior art of image wafer inspection in a new way, using prior knowledge like the design layout of VLSI wafer manufacturing process. This technique requires a golden template of the patterned wafer image under inspection which is obtained from the wafer image itself mixed to the layout design schemes. First a mapping between physical space and pixel space is needed. Then a template matching is applied for a more accurate alignment between wafer device and template. Finally, a segmented comparison is used for finding out possible defects. Results of the proposed method are presented in terms of visual quality of defect detection, any misalignment at topology level and number of correctly detected defective devices.

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“…Other methods such as optical spatial filtering methods [11], laser scattering methods [18] and wavelet technique [12] have also been involved in wafer inspection. A fairly complete review of the related literature may be found in [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A golden-block-based self-refining scheme for repetitive patterned wafer inspections

Guan

Xie

2003

Machine Vision and Applications

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This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. It has a learning ability that is able to create a golden block database from the wafer image itself, modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrives, we do not have to re-calculate its periods and building block. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. And the storage overhead of golden templates is also reduced significantly by storing golden blocks only. Index terms:Wafer inspection -Golden template -PDI -Image-to-image reference methodGolden block 2

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<title>Machine vision algorithms for semiconductor wafer inspection: a project with Inspex</title>

Cited by 7 publications

References 0 publications

A golden-template self-generating method for patterned wafer inspection

A golden-template self-generating method for patterned wafer inspection

Image Wafer Inspection based on Template Matching

A golden-block-based self-refining scheme for repetitive patterned wafer inspections

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