Automatic classification of blank substrate defects

Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark P.; Ronald, R Price; Rost, Dan; Bhamidipati, Samir

doi:10.1117/12.2069678

Cited by 2 publications

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“…To control particle defects, the presence, shape, size and composition of particles must be characterized. Advanced Defect Classification (ADC) is an established technique to install defectivity control 1 . With shrinking device dimensions, smaller and smaller particles become the root cause for critical defects.…”

Section: Introductionmentioning

confidence: 99%

Deep sub-wavelength metrology for advanced defect classification

et al. 2017

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Particle defects are important contributors to yield loss in semi-conductor manufacturing. Particles need to be detected and characterized in order to determine and eliminate their root cause. We have conceived a process flow for advanced defect classification (ADC) that distinguishes three consecutive steps; detection, review and classification. For defect detection, TNO has developed the Rapid Nano (RN3) particle scanner, which illuminates the sample from nine azimuth angles. The RN3 is capable of detecting 42 nm Latex Sphere Equivalent (LSE) particles on XXX-flat Silicon wafers. For each sample, the lower detection limit (LDL) can be verified by an analysis of the speckle signal, which originates from the surface roughness of the substrate. In detection-mode (RN3.1), the signal from all illumination angles is added. In review-mode (RN3.9), the signals from all nine arms are recorded individually and analyzed in order to retrieve additional information on the shape and size of deep sub-wavelength defects. This paper presents experimental and modelling results on the extraction of shape information from the RN3.9 multi-azimuth signal such as aspect ratio, skewness, and orientation of test defects.Both modeling and experimental work confirm that the RN3.9 signal contains detailed defect shape information. After review by RN3.9, defects are coarsely classified, yielding a purified Defect-of-Interest (DoI) list for further analysis on slower metrology tools, such as SEM, AFM or HIM, that provide more detailed review data and further classification. Purifying the DoI list via optical metrology with RN3.9 will make inspection time on slower review tools more efficient.

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Section: Introductionmentioning

confidence: 99%

Deep sub-wavelength metrology for advanced defect classification

et al. 2017

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Automatic classification of EUV mask defects inspected using DUV inspection optics

Rajiv,

Bhamidipati,

Pereira

et al. 2024

Photomask Technology 2024

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EUV lithography has been well-established over the past few years, to be the next technological milestone, to achieve lower pattern resolutions i.e. higher technology nodes. Ensuring a lower defect count however is particularly important for its use in mass-production, because of the considerably high cost of EUV masks.EUV wavelength (actinic EUV inspection) is necessary to be able to catch and characterize EUV defects correctly, particularly phase defects, originating due to imperfections in multi-layers. Defects on a patterned mask require EUV wavelengths (higher resolution) for accurate assessment of their CD impact. However, DUV optics are also employed by mask shops, along with SEM review, as a cost-effective option.Emphasizing the importance of defect handling in EUV masks, especially for EUV masks without pellicles in the memory industry, the most difficult part of EUV mask management is in controlling adder. Detecting smaller adder size is required as mask pattern size is getting smaller in higher technology nodes, which accompanies increased sensitivity of patterned mask inspection and unavoidable increase in noise signal that makes it hard to proper classify mask defects.This paper talks about the details of how DUV inspection optics are utilized to achieve an efficient pre-filtering of EUV mask defects. This, coupled with well-tested automatic defect classification algorithms of Calibre ® DefectClassify, results in a reliable solution to manage regular classification of mask defects as real or false, under such sensitive conditions.

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Automatic classification of blank substrate defects

Cited by 2 publications

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Deep sub-wavelength metrology for advanced defect classification

Deep sub-wavelength metrology for advanced defect classification

Automatic classification of EUV mask defects inspected using DUV inspection optics

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