Mr. Gilles Tabbone Profile

Gilles Tabbone

at Carl Zeiss SMT GmbH

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Publications (8)

Proceedings Article | 19 September 2018 Paper

On the road to automated production workflows in the back end of line

Gilles Tabbone, Kokila Egodage, Kristian Schulz, Anthony Garetto

Proceedings Volume 10775, 107750N (2018) https://doi.org/10.1117/12.2326908

KEYWORDS: Back end of line, Scanning electron microscopy, Inspection, Manufacturing, Photomasks, Image analysis, Defect inspection, Image segmentation

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Proceedings Article | 16 October 2017 Paper

Improving back end of line productivity through smart automation

Kristian Schulz, Kokila Egodage, Gilles Tabbone, Anthony Garetto

Proceedings Volume 10451, 1045116 (2017) https://doi.org/10.1117/12.2280832

KEYWORDS: Photomasks, Back end of line, Manufacturing, Reliability, Scanning electron microscopy, Error analysis, Optimization (mathematics), Mask making, Semiconductors, Data communications

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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.

Proceedings Article | 28 September 2017 Paper

SEM AutoAnalysis: enhancing photomask and NIL defect disposition and review

Kristian Schulz, Kokila Egodage, Gilles Tabbone, Christian Ehrlich, Anthony Garetto

Proceedings Volume 10446, 104460I (2017) https://doi.org/10.1117/12.2280828

KEYWORDS: Scanning electron microscopy, Photomasks, Nanoimprint lithography, Image analysis, Image processing, Image resolution

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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.

Proceedings Article | 13 July 2017 Paper

How smart is your BEOL? productivity improvement through intelligent automation

Kristian Schulz, Kokila Egodage, Gilles Tabbone, Anthony Garetto

Proceedings Volume 10454, 104540X (2017) https://doi.org/10.1117/12.2282804

KEYWORDS: Back end of line, Photomasks, Manufacturing, Inspection, Image processing, Image analysis, Semiconducting wafers, Image segmentation, Standards development, Data modeling

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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.

Proceedings Article | 5 October 2016 Paper

To repair or not to repair: with FAVOR® there is no question

Anthony Garetto, Kristian Schulz, Gilles Tabbone, Michael Himmelhaus, Thomas Scheruebl

Proceedings Volume 9985, 99851Q (2016) https://doi.org/10.1117/12.2243620

KEYWORDS: Manufacturing, Reliability, Tolerancing, Image processing, Image enhancement, Error analysis, Inspection, Image analysis, Back end of line, Neodymium

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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 complexity. 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.

Proceedings Article | 1 October 2013 Paper

Utilization of AIMS Bossung plots to predict Qz height deviations from nominal

Anthony Garetto, Doug Uzzel, Krister Magnusson, Jon Morgan, Gilles Tabbone

Proceedings Volume 8886, 88860K (2013) https://doi.org/10.1117/12.2031618

KEYWORDS: Etching, Quartz, Photomasks, Atomic force microscopy, Metrology, Critical dimension metrology, Inspection, Time metrology, Manufacturing, Back end of line

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Proceedings Article | 9 September 2013 Paper

A novel method for utilizing AIMS to evaluate mask repair and quantify over-repair or under-repair condition

Doug Uzzel, Anthony Garetto, Krister Magnusson, Gilles Tabbone

Proceedings Volume 8880, 888029 (2013) https://doi.org/10.1117/12.2027766

KEYWORDS: Etching, Quartz, Bridges, Atomic force microscopy, Opacity, Metrology, 3D modeling, Critical dimension metrology, Photomasks, Refractive index

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Proceedings Article | 28 June 2013 Paper

A method of utilizing AIMS to quantify substrate/attenuator over-etch or under-etch during mask repair

Vahagn Sargsyan, Kevin Olson, Doug Uzzel, Jon Morgan, Mark Ma, Gilles Tabbone, Anthony Garetto

Proceedings Volume 8701, 870109 (2013) https://doi.org/10.1117/12.2027883

KEYWORDS: Etching, Quartz, Analytical research, Critical dimension metrology, Process control, Attenuators, Atomic force microscopy, Photomasks, Opacity, Image processing

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