TY - GEN
T1 - Deep sub-wavelength metrology for advanced defect classification
AU - van der Walle, P
AU - Kramer, E.
AU - van der Donck, J.C.J.
AU - Mulckhuyse, W
AU - Nijsten, L.
AU - Bernal Arango, F. A.
AU - de Jong, A.
AU - van Zeijl, E.
AU - Spruit, H. E.T.
AU - van Den Berg, J. H.
AU - Nanda, G.
AU - Van Langen-Suurling, A. K.
AU - Alkemade, P. F.A.
AU - Pereira, S. F.
AU - Maas, D.J.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - advanced defect classification
KW - dark field microscopy
KW - defect detection
KW - defect review
KW - latex sphere equivalent
KW - Particle contamination
KW - semiconductor
KW - speckle
UR - http://resolver.tudelft.nl/uuid:99097a4e-529e-466c-ab6d-6ed5c06a85a1
UR - http://www.scopus.com/inward/record.url?scp=85029181900&partnerID=8YFLogxK
U2 - 10.1117/12.2272414
DO - 10.1117/12.2272414
M3 - Conference contribution
AN - SCOPUS:85029181900
T3 - Proceedings of SPIE
BT - Optical Measurement Systems for Industrial Inspection X
A2 - Lehmann, Peter
A2 - Osten, Wolfgang
A2 - Albertazzi Gonçalves, Armando
PB - SPIE
CY - Bellingham, WA, USA
T2 - Optical Measurement Systems for Industrial Inspection X 2017
Y2 - 26 June 2017 through 29 June 2017
ER -