Automatic classification and accurate size measurement of blank mask defects

Bhamidipati, Samir; Paninjath, Sankaranarayanan; Pereira, Mark P.; Buck, Peter

doi:10.1117/12.2198001

Cited by 3 publications

(3 citation statements)

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“…In automatic visual inspection systems, traditional approaches use the bounding box or the horizontal cross-section of the detected defect to estimate the defect size [61,62]. For surface defects, this does not represent the actual defect size and thus they recommend using the largest dimension of all cross-sections of the detected defect [62]. Those authors developed a software tool to detect and to calculate the defect characteristics, including the defect size, shape, location and type.…”

Section: Automated Defect Measurementmentioning

confidence: 99%

“…The reason for this restriction is that the defect classification was not realised and is future work. Previous research showed that a rule-based framework could be used to classify defect types [62]. The descriptor that provides mathematical morphology introduce in Section 3.2.2 is also capable to extract additional characteristics, including information about the amount of missing material and edge deformation.…”

Section: Comments On the Decision Support Toolmentioning

confidence: 99%

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Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

et al. 2021

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Background—In the field of aviation, maintenance and inspections of engines are vitally important in ensuring the safe functionality of fault-free aircrafts. There is value in exploring automated defect detection systems that can assist in this process. Existing effort has mostly been directed at artificial intelligence, specifically neural networks. However, that approach is critically dependent on large datasets, which can be problematic to obtain. For more specialised cases where data are sparse, the image processing techniques have potential, but this is poorly represented in the literature. Aim—This research sought to develop methods (a) to automatically detect defects on the edges of engine blades (nicks, dents and tears) and (b) to support the decision-making of the inspector when providing a recommended maintenance action based on the engine manual. Findings—For a small sample test size of 60 blades, the combined system was able to detect and locate the defects with an accuracy of 83%. It quantified morphological features of defect size and location. False positive and false negative rates were 46% and 17% respectively based on ground truth. Originality—The work shows that image-processing approaches have potential value as a method for detecting defects in small data sets. The work also identifies which viewing perspectives are more favourable for automated detection, namely, those that are perpendicular to the blade surface.

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Section: Automated Defect Measurementmentioning

confidence: 99%

Section: Comments On the Decision Support Toolmentioning

confidence: 99%

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

et al. 2021

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“…For example, past work have noted the discrepancy between tool-reported defect size and physically measured defect size, however compensation strategies were not explored. 20 In this article, several experiments are preformed to quantify the variability and error associated with current HVM photomask inspection and write tools. Section 2 discusses in detail how an empirical 3-sigma error budget, β, is established.…”

Section: Introductionmentioning

confidence: 99%

Defect avoidance for EUV photomask readiness at the 7 nm node

Rankin

Narita

et al. 2016

SPIE Proceedings

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Several challenges hinder extreme ultraviolet lithography (EUVL) photomask fabrication and its readiness for high volume manufacturing (HVM). The lack in availability of pristine defect-free blanks as well as the absence of a robust mask repair technique mandates defect mitigation through pattern shift for the production of defect-free photomasks. The work presented here provides a comprehensive look at pattern shift implementation to intersect EUV HVM for the 7 nm technology node. An empirical error budget to compensate for measurement variability and errors, based on the latest HVM inspection and write tool capabilities, is first established and then experimentally verified. The validated error budget is applied to 20 representative EUV blanks and pattern shift is performed on fully functional 7 nm node chip designs. The probability of defect-free masks is explored for various layers, including metal, contact, and gate cut layers. From these results, an assessment is made on the current viability of defect-free EUV masks and what is required to construct a complete defect-free EUV mask set.

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Viability of pattern shift for defect-free extreme ultraviolet lithography photomasks

Rankin

Narita³

et al. 2016

J. Micro/Nanolith. MEMS MOEMS

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Automatic classification and accurate size measurement of blank mask defects

Cited by 3 publications

References 0 publications

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

Defect avoidance for EUV photomask readiness at the 7 nm node

Viability of pattern shift for defect-free extreme ultraviolet lithography photomasks

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