The technical roadmap adopted by the semiconductor industry drives mask shops to embrace advanced solutions to overcome challenges inherent to smaller technology nodes while increasing reliability and turnaround time (TAT). It is observed that the TAT is increasing at a rapid rate for each new ground rule. At the same time, productivity and quality must be ensured to deliver the perfect mask to the customer. These challenges require optimization of overall manufacturing flows and individual steps, which can be addressed and improved via smart automation. Ideally, remote monitoring, controlling and adjusting key aspects of the production would improve labor efficiency and enhance productivity. It would require collecting and analyzing all available process data to facilitate or even automate decision-making steps. In mask shops, numerous areas of the back end of line (BEOL) workflow have room for improvement in regards to defect disposition, reducing human errors, standardizing recipe generation, data analysis and accessibility to useful and centralized information to support certain approaches such as repair. Adapting these aspects allows mask manufacturers to control and even predict the TAT that would lead to an optimized process of record.
Despite recently receiving a large amount of global publicity, smart automation is yet to be fully implemented in production for many areas, including mask making for semiconductors. One specific area that can significantly benefit from smart automation is the back end of line (BEOL) in mask manufacturing where the implementation of data driven decision making and predictive analytics can completely revolutionize our current way of working. Apart from any hardware aspect, software must adapt to the current needs of connectivity which demand the ability to handle large amounts of data, have sufficient computational resources and execute tool-to-tool communication. These requirements call for flexible and expandable software applications that increase the productivity and efficiency of backend processes. Additionally, by incorporating automated systems, businesses benefit from the reduction or elimination of losses due to human error. Given the number of human interactions within each step of the standard BEOL, such as inspection, cleaning, disposition/review and repair, mask shops run a high risk of a mishap occurring. Even by extensive measures such errors can only be reduced but not completely avoided as their origin lies in the way of how humans act. The consequences can range from harmless slip-ups up to severe manufacturing impacts which finally can lead to an economic loss. These risk levels become further multiplied as both product and workflow become more complex due to the possible repetitive cycles in the repair steps. These losses can be mitigated by the use of smart automated solutions that deliver a reduction in turnaround time (TAT) and overhead. More efficient use of operator expertise and cost reductions in data handling will improve mask shops’ productivity. Another issue that intelligent automation brings is efficient tool management. In a high volume manufacturing environment it can be challenging to maintain active monitoring of tools. Consequently, idle times and bottlenecks prevent mask shops from achieving their highest potential in terms of cycle time and reliability in delivering products on time. Having the possibility to monitor the tool clusters enables efficient delegation of operations and facilitates the optimization of workflows. The proposed model in this paper investigates the effects of defectivity complexity on the TAT in a mask shop. The inclusion of intelligent application solutions effectively address human error, bottlenecks and defect complexity reducing both TAT and TAT variability. Smart automation coupled with real time monitoring and decision making solutions help control the BEOL in a predictive manner. Therefore optimization of the BEOL workflow through intelligent automation leads to a mask production with higher reliability and higher market value.
For defect disposition and repair verification regarding printability, AIMS™ is the state of the art measurement tool in industry. With its unique capability of capturing aerial images of photomasks it is the one method that comes closest to emulating the printing behaviour of a scanner. However for nanoimprint lithography (NIL) templates aerial images cannot be applied to evaluate the success of a repair process. Hence, for NIL defect dispositioning scanning, electron microscopy (SEM) imaging is the method of choice. In addition, it has been a standard imaging method for further root cause analysis of defects and defect review on optical photomasks which enables 2D or even 3D mask profiling at high resolutions. In recent years a trend observed in mask shops has been the automation of processes that traditionally were driven by operators. This of course has brought many advantages one of which is freeing cost intensive labour from conducting repetitive and tedious work. Furthermore, it reduces variability in processes due to different operator skill and experience levels which at the end contributes to eliminating the human factor. Taking these factors into consideration, one of the software based solutions available under the FAVOR® brand to support customer needs is the aerial image evaluation software, AIMS™ AutoAnalysis (AAA). It provides fully automated analysis of AIMS™ images and runs in parallel to measurements. This is enabled by its direct connection and communication with the AIMS™tools. As one of many positive outcomes, generating automated result reports is facilitated, standardizing the mask manufacturing workflow. Today, AAA has been successfully introduced into production at multiple customers and is supporting the workflow as described above. These trends indeed have triggered the demand for similar automation with respect to SEM measurements leading to the development of SEM AutoAnalysis (SAA). It aims towards a fully automated SEM image evaluation process utilizing a completely different algorithm due to the different nature of SEM images and aerial images. Both AAA and SAA are the building blocks towards an image evaluation suite in the mask shop industry.
The back end of line (BEOL) workflow in the mask shop still has crucial issues throughout all
standard steps which are inspection, disposition, photomask repair and verification of repair
success. All involved tools are typically run by highly trained operators or engineers who setup
jobs and recipes, execute tasks, analyze data and make decisions based on the results. No matter
how experienced operators are and how good the systems perform, there is one aspect that
always limits the productivity and effectiveness of the operation: the human aspect.
Human errors can range from seemingly rather harmless slip-ups to mistakes with serious and
direct economic impact including mask rejects, customer returns and line stops in the wafer fab.
Even with the introduction of quality control mechanisms that help to reduce these critical but
unavoidable faults, they can never be completely eliminated. Therefore the mask shop BEOL
cannot run in the most efficient manner as unnecessary time and money are spent on processes
that still remain labor intensive.
The best way to address this issue is to automate critical segments of the workflow that are
prone to human errors. In fact, manufacturing errors can occur for each BEOL step where
operators intervene. These processes comprise of image evaluation, setting up tool recipes, data
handling and all other tedious but required steps. With the help of smart solutions, operators
can work more efficiently and dedicate their time to less mundane tasks. Smart solutions
connect tools, taking over the data handling and analysis typically performed by operators and
engineers. These solutions not only eliminate the human error factor in the manufacturing
process but can provide benefits in terms of shorter cycle times, reduced bottlenecks and
prediction of an optimized workflow. In addition such software solutions consist of building
blocks that seamlessly integrate applications and allow the customers to use tailored solutions.
To accommodate for the variability and complexity in mask shops today, individual workflows
can be supported according to the needs of any particular manufacturing line with respect to
necessary measurement and production steps. At the same time the efficiency of assets is
increased by avoiding unneeded cycle time and waste of resources due to the presence of
process steps that are very crucial for a given technology.
In this paper we present details of which areas of the BEOL can benefit most from intelligent
automation, what solutions exist and the quantification of benefits to a mask shop with full
automation by the use of a back end of line model.
In the mask shop the challenges associated with today’s advanced technology nodes, both
technical and economic, are becoming increasingly difficult. The constant drive to continue
shrinking features means more masks per device, smaller manufacturing tolerances and more
complexity along the manufacturing line with respect to the number of manufacturing steps
required. Furthermore, the extremely competitive nature of the industry makes it critical for
mask shops to optimize asset utilization and processes in order to maximize their competitive
advantage and, in the end, profitability.
Full maximization of profitability in such a complex and technologically sophisticated
environment simply cannot be achieved without the use of smart automation. Smart
automation allows productivity to be maximized through better asset utilization and process
optimization. Reliability is improved through the minimization of manual interactions
leading to fewer human error contributions and a more efficient manufacturing line. In
addition to these improvements in productivity and reliability, extra value can be added
through the collection and cross-verification of data from multiple sources which provides
more information about our products and processes.
When it comes to handling mask defects, for instance, the process consists largely of time
consuming manual interactions that are error prone and often require quick decisions from
operators and engineers who are under pressure. The handling of defects itself is a multiple
step process consisting of several iterations of inspection, disposition, repair, review and
cleaning steps. Smaller manufacturing tolerances and features with higher complexity
contribute to a higher number of defects which must be handled as well as a higher level of
In this paper the recent efforts undertaken by ZEISS to provide solutions which address these
challenges, particularly those associated with defectivity, will be presented. From automation
of aerial image analysis to the use of data driven decision making to predict and propose the
optimized back end of line process flow, productivity and reliability improvements are
targeted by smart automation. Additionally the generation of the ideal aerial image from the
design and several repair enhancement features offer additional capabilities to improve the
efficiency and yield associated with defect handling.
The standard method for defect disposition and verification of repair success in the mask shop is through the utilization of the aerial imaging platform, AIMS<sup>TM</sup>. The CD (Critical Dimension) deviation of the defective or repaired region as well as the pattern shift can be calculated by comparing the measured aerial images of this region to that of a reference. Through this analysis it can be determined if the defect or repaired region will be printed on the wafer under the illumination conditions of the scanner. The analysis of the measured aerial images from the AIMS<sup>TM</sup> are commonly performed manually using the analysis software available on the system or with the help of an analysis software called RV (Repair Verification). Because the process is manual, it is not standardized and is subject to operator variations. This method of manual aerial image analysis is time consuming, dependent on the skill level of the operator and significantly contributes to the overall mask manufacturing process flow. AutoAnalysis (AA), the first application available for the FAVOR® platform, provides fully automated analysis of AIMS<sup>TM</sup> aerial images  and runs in parallel to the measurement of the aerial images. In this paper, we investigate the initial AutoAnalysis performance compared to the conventional method using RV and its application to a production environment. The evaluation is based on the defect CD of three pattern types: contact holes, dense line and spaces and peripheral structure. The defect analysis results for different patterns and illumination conditions will be correlated and challenges in transitioning to the new approach will be discussed.
The decreasing size and increasing complexity of photomask features, driven by the push to ever smaller technology nodes, places more and more challenges on the mask house, particularly in terms of yield management and cost reduction. Particularly challenging for mask shops is the inspection, repair and review cycle which requires more time and skill from operators due to the higher number of masks required per technology node and larger nuisance defect counts. While the measurement throughput of the AIMS™ platform has been improved in order to keep pace with these trends, the analysis of aerial images has seen little advancement and remains largely a manual process. This manual analysis of aerial images is time consuming, dependent on the skill level of the operator and significantly contributes to the overall mask manufacturing process flow. <p> </p>AutoAnalysis, the first application available for the FAVOR® platform, offers a solution to these problems by providing fully automated analysis of AIMS™ aerial images. Direct communication with the AIMS™ system allows automated data transfer and analysis parallel to the measurements. User defined report templates allow the relevant data to be output in a manner that can be tailored to various internal needs and support the requests of your customers. Productivity is significantly improved due to the fast analysis, operator time is saved and made available for other tasks and reliability is no longer a concern as the most defective region is always and consistently captured. In this paper the concept and approach of AutoAnalysis will be presented as well as an update to the status of the project. The benefits arising from the use of AutoAnalysis will be discussed in more detail and a study will be performed in order to demonstrate.
For over 2 decades the AIM<sup>TM</sup> platform has been utilized in mask shops as the standard for actinic review of photomask sites in order to perform defect disposition and repair review. Throughout this time the measurement throughput of the systems has been improved in order to keep pace with the requirements demanded by a manufacturing environment, however the analysis of the sites captured has seen little improvement and remained a manual process. This manual analysis of aerial images is time consuming, subject to error and unreliability and contributes to holding up turn-around time (TAT) and slowing process flow in a manufacturing environment. AutoAnalysis, the first application available for the FAVOR® platform, offers a solution to these problems by providing fully automated data transfer and analysis of AIM<sup>TM</sup> aerial images. The data is automatically output in a customizable format that can be tailored to your internal needs and the requests of your customers. Savings in terms of operator time arise from the automated analysis which no longer needs to be performed. Reliability is improved as human error is eliminated making sure the most defective region is always and consistently captured. Finally the TAT is shortened and process flow for the back end of the line improved as the analysis is fast and runs in parallel to the measurements. In this paper the concept and approach of AutoAnalysis will be presented as well as an update to the status of the project. A look at the benefits arising from the automation and the customizable approach of the solution will be shown.
The EUV mask infrastructure is of key importance for the successful introduction of EUV lithography into volume production. In particular, for the production of defect free masks an actinic review of potential defect sites is required. To realize such an actinic review tool, Carl Zeiss and the SEMATECH EUVL Mask Infrastructure consortium started a development program for an EUV aerial image metrology system, the AIMS™ EUV. In this paper, we discuss the current status of the prototype integration and show recent results.
Overcoming the challenges associated with photomask defectivity is one of the key aspects associated with EUV
mask infrastructure. In addition to establishing specific EUV mask repair approaches, the ability to identify printable
mask defects that require repair as well as to verify if a repair was successful are absolutely necessary. Such
verification can only be performed by studying the repaired region using actinic light at an exact emulation of the
scanner illumination conditions of the mask as can be done by the AIMS<sup>TM</sup> EUV. ZEISS, in collaboration with the
SEMATECH EUVL Mask Infrastructure (EMI) consortium are currently developing the AIMS<sup>TM</sup> EUV system and
have recently achieved First Light on the prototype system, a major achievement. First light results will be presented
in addition to the current development status of the system.
The introduction of extreme ultraviolet (EUV) lithography into manufacturing requires changes in all aspects of the infrastructure, including the photomask. EUV reflective masks consist of a sophisticated multilayer (ML) mirror, capping layer, absorber layer, and anti-reflective coating thereby dramatically increasing the complexity of the photomask. In addition to absorber type defects similar to those the industry was forced to contend with for deep ultraviolet lithography, the complexity of the mask leads to new classes of ML defects. Furthermore, these approaches are complicated not only by the mask itself but also by unique aspects associated with the exposure of the photomask by the EUV scanner. This paper focuses on the challenges for handling defects associated with inspection, review, and repair for EUV photomasks. Blank inspection and pattern shifting, two completely new steps within the mask manufacturing process that arise from these considerations, and their relationship to mask review and repair are discussed. The impact of shadowing effects on absorber defect repair height is taken into account. The effect of mask biasing and the chief ray angle rotation due to the scanner slit arc shape will be discussed along with the implications of obtaining die-to-die references for inspection and repair. The success criteria for compensational repair of ML defects will be reviewed.
The combination of a reflective photomask with the non-telecentric illumination and arc shaped slit of the EUV scanner introduces what are known as shadowing effects. The compensation of these effects requires proper biasing of the photomask to generate the intended image on the wafer. Thus, the physical pattern on the mask ends up being noticeably different from the desired pattern to be written on the wafer. This difference has a strong dependence on both the illumination settings and the features to be printed. In this work, the impact of shadowing effects from line and space patterns with a nominal CD of 16nm at wafer was investigated with particular focus on the influence of pattern orientation and pitch, illumination pupil shape and fill (coherence) and absorber height. CD, best focus shift and contrast at best focus are utilized in detail in order to study the impact of the shadowing effects. All the simulation cases presented employ a complete scanner arc emulation, i.e. describe the impact of the azimuthal angle component of the illumination arc as in the NXE:3300 scanner and as it can be emulated by the AIMS<sup>TM</sup> EUV.
The EUV mask infrastructure is of key importance for a successful introduction of EUV lithography into volume
production. In particular, for the production of defect free masks, actinic review of potential defect sites is required. To
realize such an actinic review tool, Zeiss and the SEMATECH EUVL Mask Infrastructure consortium started a
development programme for an EUV aerial image metrology system (AIMS™ EUV). In this paper, we discuss the
status of the on-going system integration and show first results from the first light tests of the prototype tool.
The need for an actinic wavelength AIMS™ EUV tool by 2014 has been defined by SEMATECH due to the challenges
associated with EUV mask manufacture and defectivity. The AIMS™ EUV development project began in June of 2011
as a collaboration between ZEISS and the SEMATECH EUVL Mask Infrastructure (EMI) consortium. The project
remains on track to meet the first commercial tool shipment in September 2014. The current design status of the system
after two years as well as recent progress in the prototype build will be presented.
The ZEISS AIMS™ measurement system has been established for many years as the industry standard for qualifying the
printability of mask features based on the aerial image. Typical parameters in determining the printability of a feature
are the critical dimension (CD) and intensity deviations of the feature or region of interest with respect to the nominal.
While this information is critical to determine if the feature will pass printability, it gives little insight into why the
feature failed. For instance, determining if the failure occurs due to the quartz level deviating from that of the nominal
height can be problematic.
Atomic force microscopy (AFM) is commonly used to determine such physical dimensions as the quartz etch depth or
height and sidewall roughness for verification purposes and to provide feedback to front end processes. In addition the
AFM is a useful tool in monitoring and providing feedback to the repair engineers as the depth of the repair is one of the
many critical parameters which must be controlled in order to have a robust repair process.
In collaboration with Photronics nanoFab, we have previously shown the Bossung plot obtained from the AIMS™ aerial
image of a feature can be used to determine if the quartz level of a repaired region is above or below the nominal value.
This technique can further be used to extract the etch time associated with the nominal quartz height in order to optimize
the repair process. The use of this method can be used in lieu of AFM, effectively eliminating the time and effort
associated with performing additional metrology steps in a separate system. In this paper we present experimental
results supporting the technique and its applicability.
The ZEISS AIMS™ platform is well established as the industry standard for qualifying the printability of mask features
based on the aerial image. Typically the critical dimension (CD) and intensity at a certain through-focus range are the
parameters which are monitored in order to verify printability or to ensure a successful repair. This information is
essential in determining if a feature will pass printability, but in the case that the feature does fail, other metrology is
often required in order to isolate the reason why the failure occurred, e.g., quartz level deviates from nominal.
Photronics-nanoFab, in collaboration with Carl Zeiss, demonstrate the ability to use AIMS<sup>TM</sup> to provide quantitative
feedback on a given repair process; beyond simple pass/fail of the repair. This technique is used in lieu of Atomic Force
Microscopy (AFM) to determine if failing post-repair regions are "under-repaired” (too little material removed) or
“over-repaired” (too much material removed).
Using the ZEISS MeRiT E-beam repair tool as the test platform, the AIMS<sup>TM</sup> technique is used to characterize a series
of opaque repairs with differing repair times for each. The AIMS<sup>TM</sup> technique provides a means to determine the etch depth based on through-focus response of the Bossung plot and further to predict the amount of MeRiT® recipe change required in order to bring out of spec repairs to a passing state.
The ZEISS AIMS™ platform is well established as the industry standard for qualifying the printability of mask
features based on the aerial image. Typically the critical dimension (CD) and intensity at a certain through-focus
range are the parameters which are monitored in order to verify printability or to ensure a successful
repair. This information is essential in determining if a feature will pass printability, but in the case that the
feature does fail, other methods are often required in order to isolate the reason why the failure occurred,
e.g., quartz level deviation from nominal.
Atomic force microscopy (AFM) is typically used to determine physical dimensions such as the quartz etch
depth and sidewall profile. In addition the AFM is a useful tool in monitoring and providing feedback to the
repair engineer, as the depth of the repair is one of the many critical parameters which must be controlled in
order to have a robust repair process.
Carl Zeiss, in collaboration with Photronics-nanoFab, demonstrate the ability to use AIMS<sup>TM</sup> to provide
quantitative feedback on a given repair process; beyond simple pass/fail of the repair. Using the ZEISS MeRiT<sup>®</sup>
repair tool as the example, the AIMS<sup>TM</sup> technique is used in lieu of an AFM to determine if repaired regions are
over-etched or under-etched; and further to predict the amount of MeRiT<sup>®</sup> recipe change required in order to
bring subsequent repairs to a passing state.
In previous conferences the status of the AIMS™ EUV project has been presented in which the basic layout scheme and preliminary design have been shown along with the targeted performance specification levels to be met. Presently the final design milestone of the project has been successfully completed and assembly of the prototype
tool is underway. The final design concept will be presented along with the current status of the tool and simulated performance data.
The mask industry has recently witnessed an increasing number of new MoSi mask blank materials which are quickly
replacing the older materials as the standard in high end mask shops. These new materials, including OMOG (opaque
MoSi on glass) and high transmission (Hi-T) films, are driven foremost by the need to reduce feature size through
resolution enhancement techniques (RET). The subject of this paper is a new low stress, Hi-T material which addresses
the challenges presented by transitioning to smaller technology nodes including difficulties with pattern transfer,
cleaning and repair. This material, based on currently employed MoSi films, eliminates process steps and utilizes a
thinner overall substrate stack than currently used Hi-T schemes allowing an increase in critical dimension (CD)
uniformity and feature resolution and more robustness due to a lower aspect ratio. While this new material is MoSi
based the small compositional change requires, in some cases, a significant change in processing. Among the most
impacted areas are the etch, clean and repair steps. Given the potential for defects to manifest on masks, repair is an
invaluable step that can significantly impact the overall yield and lead to a reduction in cycle time<sup>1</sup>. The Carl Zeiss
MeRiT® electron beam mask repair line provides the most advanced repair capabilities allowing a wide range of repairs
to be performed on a number of mask types<sup>2</sup>. In a joint effort between MP Mask Technology Center LLC and Carl Zeiss
SMT, this paper focuses on the benefits of the new Hi-T mask blank and the challenges it presents to the repair
community. The differences between the new low stress, Hi-T material and current Hi-T technologies are presented and
on site compositional analysis is performed with x-ray photoelectron spectroscopy (XPS) to illuminate the compositional
differences. The development of a repair process for the new material utilizing the on-site Carl Zeiss MeRiT® MG 45 is
presented along with several repairs and their AIMS<sup>TM</sup> results.
The push toward smaller feature size at 193 nm exposure has been enabled by resolution enhancement techniques (RET)
such as phase shifting technologies and optical proximity correction (OPC) which require more costly and time intensive
resources to fabricate. This leads to a higher overall cost associated with each mask, making it more important than ever
for the mask shop to fully utilize and improve its repair capabilities as the presence of defects on the final product is the
major yield reducing factor. An increase in repair capability leads to a direct enhancement in repair yield which
translates to an improvement in overall mask yield and a reduction in cycle time. The Carl Zeiss MeRiT® MG 45
provides numerous benefits over other techniques that can lead to an increase in repair yield. This paper focuses on
methods utilizing the MeRiT® MG 45 that can be employed in a production environment in order to increase mask repair
yield. The capability to perform multiple repairs at a single site without optical degradation enables defects that were not
successfully repaired the first time to be corrected on a subsequent attempt. This not only provides operator mistakes
and inexperience to be corrected for, but eliminates the need to hold up production in order to start a new mask which
can cause a cascading effect down the line. Combining techniques to approach difficult partial height and combination
defects that may have previously been classified as non-repairable is presented in an attempt to enable a wider range of
defects to be repaired. Finally, these techniques are validated by investigating their impact in a production environment
in order to increase overall mask yield and decrease cycle time.
In today's economic climate it is critical to improve mask yield as materials, processes and tools are more time and cost
involved than ever. One way to directly improve mask yield is by reducing the number of masks scrapped due to defects
which is one of the major mask yield reducing factors. The MeRiT<sup>TM</sup> MG 45, with the ability to repair both clear and
opaque defects on a variety of masks, is the most comprehensive and versatile repair tool in production today. The cost
of owning multiple repair tools can be reduced and time is saved when fast turnaround is required, especially when more
than one defect type is present on a single mask. This paper demonstrates the ability to correct repair errors due to
human mistakes and presents techniques to repair challenging production line defects with the goal of maximizing mask
repair yield and cycle time reduction.
The cost and time associated with the production of photolithographic masks continues to grow, driven by the ever
decreasing feature size, advanced mask technologies and complex resolution enhancing techniques. Thus employment of
a high-resolution, comprehensive mask repair tool becomes a key element for a successful production line. The MeRiT®
utilizes electron beam induced chemistry to repair both clear and opaque defects on a variety of masks and materials with
the highest available resolution and edge placement precision. This paper describes the benefits of the electron beam
induced technique as employed by the MeRiT® system for a production environment.