Abstract
Predictable and fast production launch of new products (product ramp-up) is a crucial success factor in the production industry in general, and for the production of integrated circuits (ICs) in particular. During the ramp-up phase of the product there is, inter alia, the need for product-specific configuration of a wide range of software systems that control the production process in a fully automated manner. This collection of software systems is sourced from several vendors and has therefore to be configured in different ways. Moreover, configuration has to be orchestrated along the whole production process, in accordance with the needs of the new product. This is a complicated, error-prone, and time-consuming task for product engineers, process engineers, and application engineers. The approach described in this chapter avoids such efforts and risks through a semiautomated generation of configurations of software systems. It uses a knowledge base which provides a unified configuration schema across all involved software systems. The approach applies automated reasoning of new configuration content based on the knowledge about new products’ characteristics and knowledge about the existing production environment. The knowledge base is described by ontology models and based on Semantic Web technologies. The described approach is the basis for IT professionals of IC factories and of factories with comparable IT infrastructure to standardize the configuration of software systems being in charge for production control and process control. It contributes to accelerate the launch of new products and to make their ramp-up phase more deterministic.
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Acknowledgment
This work was partly supported by the Christian Doppler Forschungsgesellschaft, the Federal Ministry of Economy, Family and Youth, and the National Foundation for Research, Technology and Development in Austria.
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Willmann, R., Kastner, W. (2016). Product Ramp-up for Semiconductor Manufacturing Automated Recommendation of Control System Setup. In: Biffl, S., Sabou, M. (eds) Semantic Web Technologies for Intelligent Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-41490-4_9
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