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.

To qualify tools of semiconductor manufacturing, particles unintentionally deposited in these tools are character-ized using blank wafers. With fast optical inspection tools one can quickly localize these particle defects. An ex-ample is TNO's Rapid Nano, which operates in optical darkfield. The next step is defect review for further defectcharacterization. When the blank wafers are transferred to another tool, e.g. a SEM or an AFM the absolute defectposition information is lost. Therefore, the re-detection of the defects in the review tool is time consuming. Toenhance the re-detection speed, afiducial marker system can be used that couples the coordinates of the fastinspection tool to the coordinates of the characterization (review) tool.In this work such afiducial marker system was designed and validated. The influences of the height and the com-position of thefiducial markers on the performance of the marker system were investigated usingfinite elementanalysis (by COMSOL) and experiments. The optimizedfiducial markers are very visible in opticalbrightfield andin SEM, while almost invisible (“stealth”) in optical darkfield. These properties make the markers both easily vis-ible and accurately localizable in the characterization tools. The stealthfiducialmarker system was fabricatedandvalidated by re-detecting programmed test defects on a blank wafer. The experimental results are compared to aMonte Carlo simulation that takes into account the uncertainties in the coordinate transformation and localiza-tion of the test defects.Our results show that afiducial marker system greatly enhances the re-detection efficacy of defects on blank wa-fers. Using thefiducial marker system, 100% of the test defects were re-detected in SEM and AFM. A single7×7μm2SEM image suffices to meet the ITRS requirement for particles as small as 70 nm in diameter.