Paper
13 July 2017 How smart is your BEOL? productivity improvement through intelligent automation
Kristian Schulz, Kokila Egodage, Gilles Tabbone, Anthony Garetto
Author Affiliations +
Abstract
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kristian Schulz, Kokila Egodage, Gilles Tabbone, and Anthony Garetto "How smart is your BEOL? productivity improvement through intelligent automation", Proc. SPIE 10454, Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology, 104540X (13 July 2017); https://doi.org/10.1117/12.2282804
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Cited by 1 scholarly publication.
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KEYWORDS
Back end of line

Photomasks

Manufacturing

Inspection

Image processing

Image analysis

Data modeling

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