&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

(5 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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“…This method is very fast but a major disadvantage is that small defects cannot be recognized. A wavelet technique [9] has also been involved in wafer inspection. Complete reviews of the related literature were performed by Babian [10], Newman [11], Moganti [12] and Klaus [13].…”

Section: Introductionmentioning

confidence: 99%

Classification of Defects on Semiconductor Wafers Using Priority Rules

Shankar¹,

Zhong²,

Ravi³

2004

DDF

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This paper presents a template-based vision system to detect and classify the nonuniformaties that appear on the semiconductor wafer surfaces. Design goals include detection of flaws and correlation of defect features based on semiconductor industry expert’s knowledge. The die pattern is generated and kept as the reference beforehand from the experts in the semiconductor industry. The system is capable of identifying the defects on the wafers after die sawing. Each unique defect structure is defined as an object. Objects are grouped into user-defined categories such as chipping, metallization peel off, silicon dust contamination, etc., after die sawing and micro-crack, scratch, ink dot being washed off, bridging, etc., from the wafer. This paper also describes the vision system in terms of its hardware modules, as well as the image processing algorithms utilized to perform the functions.

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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

Classification of Defects on Semiconductor Wafers Using Priority Rules

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