Automatic defect classification: status and industry trends

Bennett, Marylyn Hoy; Tobin, Kenneth W.; Gleason, Shaun S.

doi:10.1117/12.209203

Cited by 20 publications

(8 citation statements)

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“…Beyond database infrastructure and merging issues comes new methods that attribute informational content to data, e.g., the assignment of defect class labels through ADC [7], or unique signature labels in the population of defects distributed across the wafer using SSA [8]. These methods put the defect occurrence into a context that can later be associated with a particular process or even a corrective action.…”

Section: Yield Management Automation Trendsmentioning

confidence: 99%

Using historical wafermap data for automated yield analysis

Tobin¹,

Karnowski²,

Gleason³

et al. 1999

Journal of Vacuum Science &Amp; Technology A: Vacuum, Surfaces, and Films

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To be productive and profitable in a modern semiconductor fabrication environment, large amounts of manufacturing data must be collected, analyzed, and maintained. This includes data collected from in-line and off-line wafer inspection systems and from the process equipment itself. This data is increasingly being used to design new processes, control and maintain tools, and to provide the information needed for rapid yield learning and prediction. Because of increasing device complexity, the amount of data being generated is outstripping the yield engineer's ability to effectively monitor and correct unexpected trends and excursions. The 1997 SIA National Technology Roadmap for Semiconductors highlights a need to address these issues through "automated data reduction algorithms to source defects from multiple data sources and to reduce defect sourcing time." SEMATECH and the Oak Ridge National Laboratory † have been developing new strategies and technologies for providing the yield engineer with higher levels of assisted data reduction for the purpose of automated yield analysis. In this paper, we will discuss the current state of the art and trends in yield management automation.

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Section: Yield Management Automation Trendsmentioning

confidence: 99%

Using historical wafermap data for automated yield analysis

Tobin¹,

Karnowski²,

Gleason³

et al. 1999

Journal of Vacuum Science &Amp; Technology A: Vacuum, Surfaces, and Films

Self Cite

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“…The difference of a plurality of images in the same scene can be expressed: different resolution, different gradation attribute, different position (translation and revolving), different scale, different nonlinear transformation and so on. In order to analyze the different scenarios, it requires integrating data from those images, and the image-matching criterion is the critical step for the fusion data [2].…”

Section: Introductionmentioning

confidence: 99%

Research On Chips’ Defect Extraction Based On Image-Matching

Zhang

Xue

et al. 2014

International Journal on Smart Sensing and Intelligent Systems

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Image-matching is a basic task in image procession used to (geometrically) match two or more images such as different times, different sensors and different view points. Improved image-matching technology has been widely applied in medical image, remote sensing image, computer vision military and so on. What put forward at the present can be most divided into matching method based on gray and features. The article presents a scheme that is based on subtraction images matching with dynamic selection of templates, which overcomes these more sensitive disadvantages like gray, rotate, deformation and occlusion to achieve accurate calibration for different images.

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“…These categories are typically associated with process steps through historical data. ADC therefore has an excellent chance of identifying the source of a fault-causing defect [31].…”

Section: Automatic Defect Classification Trainability and False Amentioning

confidence: 99%

Quantifying the value of ownership of yield analysis technologies

Weber¹,

Sankaran

Scher

et al. 2002

IEEE Trans. Semicond. Manufact.

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Abstract-A model based on information theory, which allows yield managers to determine optimal portfolio of yield analysis technologies for both the R&D and volume production environments, is presented. The information extraction per experimentation cycle and information extraction per unit time serve as benchmarking metrics for yield learning. They enable yield managers to make objective comparisons of apparently unrelated technologies. Combinations of four yield analysis tools-electrical testing, automatic defect classification, spatial signature analysis and wafer position analysis-are examined in detail to determine the relative value of ownership of different yield analysis technologies.

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Automatic defect classification: status and industry trends

Cited by 20 publications

References 0 publications

Using historical wafermap data for automated yield analysis

Using historical wafermap data for automated yield analysis

Research On Chips’ Defect Extraction Based On Image-Matching

Quantifying the value of ownership of yield analysis technologies

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