Proceedings Article | 9 September 2013
Paul Morgan, Daniel Rost, Daniel Price, Noel Corcoran, Masaki Satake, Peter Hu, Danping Peng, Dean Yonenaga, Vikram Tolani, Yulian Wolf, Pinkesh Shah
KEYWORDS: Inspection, Photomasks, Contamination, Defect detection, Resolution enhancement technologies, Defect inspection, Particles, Image classification, Air contamination, Scanners
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.
