Improvement in accuracy of defect size measurement by automatic defect classification
Abstract
The blank mask defect review process involves detailed analysis of defects observed across a substrate’s multiple preparation stages, such as cleaning and resist-coating. The detailed knowledge of these defects plays an important role in the eventual yield obtained by using the blank. Defect knowledge predominantly comprises of details such as the number of defects observed, and their accurate sizes. Mask usability assessment at the start of the preparation process, is crudely based on number of defects. Similarly, defect size gives an idea of eventual wafer defect printability. Furthermore, monitoring defect characteristics, specifically size and shape, aids in obtaining process related information such as cleaning or coating process efficiencies. Blank mask defect review process is largely manual in nature. However, the large number of defects, observed for latest technology nodes with reducing half-pitch sizes; and the associated amount of information, together make the process increasingly inefficient in terms of review time, accuracy and consistency. The usage of additional tools such as CDSEM may be required to further aid the review process resulting in increasing costs. Calibre® MDPAutoClassify™ provides an automated software alternative, in the form of a powerful analysis tool for fast, accurate, consistent and automatic classification of blank defects. Elaborate post-processing algorithms are applied on defect images generated by inspection machines, to extract and…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
For surface defects, the bounding box or horizontal cross-section does not represent the actual defect size, and the largest dimension of all cross-sections is recommended.
“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].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [62]
Traditional automatic visual inspection approaches use the bounding box or the horizontal cross-section of the detected defect to estimate defect size.
“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].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [61]
Traditional automatic visual inspection approaches use the bounding box or the horizontal cross-section of the detected defect to estimate defect size.
“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].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [62]
Previous research showed that a rule-based framework could be used to classify defect types.
“Previous research showed that a rule-based framework could be used to classify defect types [62].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [62]
Cited by
Sorted by citations
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
“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].”
Method
- Authors
- Samir, B., Pereira, M., Paninjath, S., Jeon, C.-U., Chung, D.-H., Yoon, G.-S., & Jung, H.-Y.
- Affiliations
- Samsung Electronics (South Korea) (KR)
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2015-10-23
- Volume
- 9635
- Pages
- 963520
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2179682871
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 2 · 2022: 1 · 2021: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Photolithography Techniques
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- defect size measurement, automatic defect classification, photomask inspection, defect printability, defect detection, noise reduction, defect localization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review