Universität Siegen (USI)
Universität Siegen (University of Siegen) is located in the state of North-Rhine-Westfalia, Germany. About 20,000 student participate in graduate and undergraduate programmes. The two chairs taking part in iDev40 belong to the largest faculty “Science and Technology”.
The University of Siegen (USI), founded in 1972, is a modern institution of the higher education located centrally in the area bordering the three German federal states of Hesse, North Rhine-Westphalia and Rhineland-Palatinate. USI has four faculties with more than 19,414 students and 3,862 employees.
The research is carried out in faculty of Sciences and Technology which is divided into 12 departments. The participating research groups belong to the Dept. Electrical Engineering and Computer Science. University of Siegen, takes part with two research groups.
The Institute for Knowledge Based Systems and Knowledge Management (KBS) holds expertise in the European, national and industry funded projects in the domain of applied knowledge management and especially intelligent systems and data/text mining, machine learning, semantic technologies and software development. A strong application focus is in embedded systems, (semiconductor) manufacturing, medical applications, and e-health and higher education, all combined with a strong analytical focus.
The Medical Informatics and Microsystem Engineering Group (MIM) is working on methods and tools for semiconductor technology development and design as well as test technology for more than 15 years. The current activities cover three areas: tools and methodologies for design, manufacturing and test, medical informatics and system integration and communication. MIM group will contribute with the development of yield prediction strategies and methodology, MEMS manufacturing process modelling as well as yield cause modelling.
USI has 123 international partnerships and 150 ERASMUS partner universities across Europe and a long history in EU projects. USI has a focus on fostering international networks and as a consequence, 11% of the students and academics are from other countries.
USI offers comprehensive local training in the House of Young Talents (HYT), which is established in 2016, an intensive and complete institutionalized coaching and fostering of PhD candidates, but also postdocs and junior faculty members is afforded with the central objective to implement targeted efforts for personalized advice and holistic career advancement. The University offers additional optional courses in academic writing, written and spoken communication, ethics, career development, IPR, start-up companies etc.
USI will research, implement and disseminate methods to extract, process and interlink resources, resource extracts and structured data in the context of the product design and production flow (dynamic knowledge base and knowledge network). The focused research and implemented methods will back the WP1 Objective 1 for data management and automatic knowledge base update and support WP2 for predictive tasks in the context of virtual engineering and smart collaboration and further close the knowledge loop. Furthermore USI will take part in the dissemination and exploitation activities of the consortium.
USI will be responsible for the methodology of a yield prediction system comprising the analyzing tasks as well as the development of the algorithms taking into account the result of WP 1 and end in in a system prototype implementation
USI will also contribute to WP1 with a focus on extracting and contextualizing resources to extract meaningful information (data mining/text mining) for a dynamic information retrieval process and fusing it with additionally structured data sources to offer one consolidated information landscape with human and automated interface to fuel analytical and predictive processes. The goal is to create a dynamic knowledge base out of sets of unstructured documents and structured and semi-structured resources (reports, project documents, design rules, QM-regulations, process-descriptions, emails, database contents, FMEAs), which will then be a product flow detailing and project flow and context-aware fundament for intelligent information retrieval and as such furthermore support the focused predictive analysis in WP2.