9 September 2013 Computational mask defect review for contamination and haze inspections
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Abstract
As optical lithography continues to extend into sub-0.35 k1 regime, mask defect inspection and subsequent review has become tremendously challenging, and indeed the largest component to mask manufacturing cost. The routine use of various resolution enhancement techniques (RET) have resulted in complex mask patterns, which together with the need to detect even smaller defects due to higher MEEFs, now requires an inspection engineer to use combination of inspection modes. This is achieved in 193nm AeraTM mask inspection systems wherein masks are not only inspected at their scanner equivalent aerial exposure conditions, but also at higher Numerical Aperture resolution, and special reflected-light, and single-die contamination modes, providing better coverage over all available patterns, and defect types. Once the required defects are detected by the inspection system, comprehensively reviewing and dispositioning each defect then becomes the Achilles heel of the overall mask inspection process. Traditionally, defects have been reviewed manually by an operator, which makes the process error-prone especially given the low-contrast in the convoluted aerial images. Such manual review also limits the quality and quantity of classifications in terms of the different types of characterization and number of defects that can practically be reviewed by a person. In some ways, such manual classification limits the capability of the inspection tool itself from being setup to detect smaller defects since it often results in many more defects that need to be then manually reviewed. Paper 8681-109 at SPIE Advanced Lithography 2013 discussed an innovative approach to actinic mask defect review using computational technology, and focused on Die-to-Die transmitted aerial and high-resolution inspections. In this approach, every defect is characterized in two different ways, viz., quantitatively in terms of its print impact on wafer, and qualitatively in terms of its nature and origin in the mask manufacturing process. The latter characterization qualifies real defect signatures, such as pin-dots or pin-holes, extrusions or intrusions, assist-feature or dummy-fill defects, writeerrors or un-repairable defects, chrome-on-shifter or missing chrome-from-shifter defects, particles, etc., and also false defect signatures, such as those due to inspection tool registration or image alignment, interlace artifacts, CCD camera artifacts, optical shimmer, focus errors, etc. Such qualitative characterization of defects has enabled better inspection tool SPC and process defect control in the mask shop. In this paper, the same computational approach to defect review has been extended to contamination style defect inspections, including Die-to-Die reflected, and non Die-to-Die or single-die inspections. In addition to the computational methods used for transmitted aerial images, defects detected in die-to-die reflected light mode are analyzed based on special defect and background coloring in reflected-light, and other characteristics to determine the exact type and severity. For those detected in the non Die-to-Die mode, only defect images are available from the inspection tool. Without a reference, i.e., defect-free image, it is often difficult to determine the true nature or impact of the defect in question. Using a combination of inspection-tool modeling and image inversion techniques, Luminescent’s LAIPHTM system generates an accurate reference image, and then proceeds with automated defect characterization as if the images were simply from a die-to-die inspection. The disposition of contamination style defects this way, filters out >90% of false and nuisance defects that otherwise would have been manually reviewed or measured on AIMSTM. Such computational defect review, unifying defect disposition across all available inspection modes, has been imperative to ensuring no yield losses due to errors in operator defect classification on one hand, and on the other, has enhanced defect characterization and detection capability of the inspection platform itself notwithstanding the number of defects detected in the process.
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Paul Morgan, Daniel Rost, Daniel Price, Noel Corcoran, Masaki Satake, Peter Hu, Danping Peng, Dean Yonenaga, Vikram Tolani, Yulian Wolf, Pinkesh Shah, "Computational mask defect review for contamination and haze inspections", Proc. SPIE 8880, Photomask Technology 2013, 88800L (9 September 2013); doi: 10.1117/12.2027648; https://doi.org/10.1117/12.2027648
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