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 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.