Most leading-edge IC fabs continue to use direct reticle inspection for "early warning" detection of haze defects before
they print on wafers. This inspection strategy enables fabs to cost-effectively maintain the highest product yields
possible. As design rules advance from 45/40 nm nodes to 32/28 nm, mask pattern sizes continue to shrink while
increasing in pattern density. More layers are exposed on 193nm immersion scanners, and as a result, reticle requal
inspection requirements become more challenging in order to meet sensitivity and inspectibility performance.
In this paper, we examine some of the inspection challenges 32/28 nm logic mask designs present. New reticle requal
requirements created by aggressive SRAF and higher MEEF mask designs used at these nodes are first examined. A
new and improved inspection technology to support requal requirements at this level is introduced and tested. These
data are analyzed to evaluate the overall inspection capability and sensitivity of this new product designed to meet 32/28
nm foundry reticle requal needs for high-volume production in IC fabs.
To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection
system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced
operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or
fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer
defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer
basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a
very tedious and time-consuming task and may cause extended manufacturing line-down situations.
Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports
to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation
errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be
spent working on other more productive activities.
This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a
format compatible with KLA-Tencor's Klarity Defect(R) data analysis database. The objective is to use the graphical
charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or
entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle
defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing
reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.
To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection
system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced
operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or
fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer
defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer
basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a
very tedious and time-consuming task and may cause extended manufacturing line-down situations.
Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports
to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation
errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be
spent working on other more productive activities.
This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a
format compatible with KLA-Tencor's Klarity DefectTM data analysis database. The objective is to use the graphical
charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or
entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle
defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing
reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.
To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection
system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced
operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or
fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer
defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer
basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a
very tedious and time-consuming task and may cause extended manufacturing line-down situations.
Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports
to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation
errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be
spent working on other more productive activities.
This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a
format compatible with KLA-Tencor's Klarity DefecTM data analysis database. The objective is to use the graphical
charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or
entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle
defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing
reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.
The ORIONTM series of test reticles have been used for many years as the photomask industry standard for evaluating contamination inspection algorithms. The deposition of Polystyrene Latex (PSL) spheres on various reticle pattern
designs allow STARlightTM tool owners to measure the relative contamination inspection performance in a consistent and quantifiable manner. However, with recent inspection technology advances such as shorter laser (light source)
wavelengths and smaller inspection pixels, PSL spheres were observed to physically degrade over relatively short time
periods: especially for the smallest sized spheres used to characterize contamination inspection performance at the most
advanced technology nodes.
Investigations into using alternative materials or methods that address the issue of PSL shrinkage have not yet proven
completely successful. Problems such as failure to properly adhere to reticle surfaces or identification of materials that
can produce consistent and predictable sphere sizes for the reliable manufacture of these critical test masks are only some
of the challenges that must be solved. Even if these and other criteria are met, the final substance must appear to
inspection optics as pseudo soft defects which resemble actual contamination that inevitably appears on production
reticle surfaces.
In the interim, programmed pindot defects present in the quartz region of the SPICATM test reticle are being used to characterize contamination performance while a suitable long-term solution to address the issue of shrinking PSL
spheres on ORION masks can be found. This paper examines the results of a programmed pindot test reticle specifically
designed to evaluate contamination algorithms without the deposition of PSL spheres or similar structures. This
alternative programmed pindot test reticle uses various background patterns similar to the ORION, however, it also
includes multiple defects sizes and locations making it more desirable than the limited range of defects found on the
SPICA.
Tritone reticle designs present many challenges for both photomask manufacturers and defect inspection equipment suppliers. From a fabrication standpoint, multi-write and process steps for tritone layers add levels of complexity and increased cost not encountered with most traditional binary (two tone) masks. For inspection tools, the presence of three distinctive light levels presents a challenge for algorithms originally designed to inspect gray scale data between two tones (black and white): especially for database transmitted light modes. While most die-to-die and STARlightTM inspections on tritone reticles produce successful results using binary algorithms, database inspections typically require two separate recipes to reveal all lithographically significant defects. With this dual-inspection technique, DNIR (Do Not Inspect Regions) are often added to eliminate the presence of third tone (typically Chrome) features: a process that adds considerable time to recipe creation. Additional workarounds when using binary inspection algorithms include implementing special light calibration techniques during setup in an effort to minimize nuisance defects caused by the presence of a third tone. As a result of these workarounds, reticle throughput is either reduced or sensitivity compromised when using binary database inspection algorithms on tritone reticles. This paper examines the benefits of using a tritone database inspection algorithm from both productivity and sensitivity standpoints as compared to results obtained from using the aforementioned workarounds and existing binary inspection modes. The results and conclusions contained within are based on data obtained from standard test vehicles and a variety of tritone production reticles.
High resolution mask inspection in advanced wafer fabs is a necessity. Initial and progressive mask defect problem still remains an industry wide mask reliability issue. Defect incidences and its criticality vary significantly among the type of masks, technology node and layer, fab environment and mask usage. A usage and layer based qualification strategy for masks in production need to be adopted in wafer fabs.
With the help of a high-resolution direct reticle inspection, early detection of critical and also non-critical defects at high capture rates is possible. A high-resolution inspection that is capable of providing necessary sensitivity to critical emerging defects (near edge) is very important in advanced nodes. At the same time, a way to disposition (make a go / no-go decision) on these defective masks is also very important. As the impact of these defects will depend on not only their size, but also on their transmission and MEEF, various defect types and characteristics have to be considered.
In this technical report the adoption of such a high-resolution mask inspection system in wafer fab production is presented and discussed. Data on this work will include inspection results from advanced masks, layer and product based inspection pixel assignment, defect disposition and overall wafer fab strategies in day-to-day production towards mask inspection.
Recent interest and inclusion to the ITRS roadmap for the investigation of NIL (Nano Imprint Lithography) has brought back to life 1X mask making. Not only does NIL require 1X pattering, it also requires physical contact with the patterning media, which, for obvious reasons, raises defectivity concerns. NIL is capable of reproducing features in the 50-10nm range, and possibly below, creating extensive manufacturing challenges for NIL tooling. KLA-Tencor has partnered with Molecular Imprints Inc. of Austin, Texas to study the eventual implementation and commercialization of NIL, especially as it pertains to the IC segment of the market. Photronics Labs Inc. is also involved in the NIL effort by developing and understanding the issues required for successfully producing commercially available tooling for this new lithography technique. Much of this work supported by NIST project #00-00-5853.
CPLTM Technology is a promising resolution enhancement technique (RET) to increase the lithography process window at small feature line widths. Successful introduction of a reticle based RET needs to address several reticle manufacturing areas. One key area is reticle inspection. A CPL reticle inspection study has been completed and a best known methodology (BKM) devised. Use of currently available inspection tools and options provides a robust solution for die-to-die inspection. Die-to-database inspection challenges and solutions for optically completed CPL reticles are discussed.
Core to the devised BKM is the concept of in-process inspections where the highest sensitivity inspection may not necessarily be performed after the last manufacturing step. The rationale for this BKM is explained in terms of actual manufacturing process flow and most likely defect sources. This rationale also has implications for programmed defect test mask designs in that the choice of defect types need to be linked to a plausible source in the manufacturing process. Often, the choice of a programmed defect type ignores the fact that a naturally occurring defect's origin is early in the manufacturing process and would be detected and either repaired or the reticle rejected before subsequent manufacturing steps. Therefore, certain programmed defect types may not be representative of what should be expected on a production mask. Examples such defects are discussed.
As AAPSM becomes more widely utilized, the need for defect inspection sensitivity becomes more critical. In addition, accurate defect characterization must be performed to encompass new effects caused by glass defects. Historically, defect size and position have been the two characteristics that were examined when determining inspection tool sensitivity. Because of the nature of AAPSM defects, phase is a factor that must be taken into account. This experiment utilizes two distinct forms of defect characterization -- SEM sizing, and surface profilometry. Programmed defect test masks were manufactured for phase shifting properties at both 248nm and 193nm exposure wavelengths. The defects were also etched at multiple depths resulting in a variety of phase angle errors. Utilizing the two characterization methods mentioned above, the automatic defect inspection tool's sensitivity on multiple programmed defects will be investigated.
Current manufacturing techniques for advanced wafers require reticle patterns to contain a variety of OPC structures. These structures include several types and sizes of serifs and assist bars creating many technical challenges for reticle inspection. While these OPC structures have evolved over the past few years, so has KLA-Tencor’s inspection algorithm product line. Photronics and KLA-Tencor are jointly examining the performance of two main algorithms (AOP and ATSdb) regarding their ability to inspect several production reticles containing various serifs and assist bars. By examining the results of these algorithms, their ability to inspect OPC reticle patterns can be compared. Part of the test criteria includes examining the number of real and nuisance defects produced by each inspection. To inspect a variety of serifs and assist bars, each algorithm requires their default sensitivity settings to be modified. These sensitivity settings are then used to inspect a Verimask to reveal their relative sensitivity capabilities. Finally, by comparing the sensitivity results from these Verimask inspections, further analysis of the performance for these algorithms can be accomplished.
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