DGM-Further Education: Artificial Intelligence in Materials Development and Process Control
High-resolution analytical techniques are essential for the development
and introduction of new nanotechnologies and thin-film technologies as
well as for the integration of advanced materials into high-tech products
and particularly for process control and for quality assessment.
A comprehensive materials analysis is more and more needed for process monitoring
during manufacturing of micro- and nanostructured systems and devices as well as for the understanding of the microstructure in materials. Therefore, research and development in the field of physical analysis increasingly focused on the study of thin films as well as micro- and nanostructures. Application-specific developments show often that the combination of several analysis techniques is needed to ensure both process control in nanotechnology as well as performance and reliability of new products. In this course, we will explain that the digital transformation in materials science and engineering requires and enables more and more the application of algorithms of artificial intelligence, particularly for new platforms, standards and technologies for data processing, data exchange and data analysis in materials and process characterization. Consequently, future tasks of scientists and engineers will include collecting and interpretation of data, in addition to the development and application of advanced techniques for materials analysis.
The course will provide knowledge in the fields of materials characterization, process control and artificial intelligence. After an introduction into materials analysis in the lab and for process monitoring as well as into artificial intelligence, advanced techniques for the characterization of thin films, nanostructures and nanoparticles will be explained. New results from fundamental research will be presented, and application-specific solutions in in the fields of microelectronics, renewable energies and lightweight construction will be demonstrated. The potential of the use of algorithms of machine learning in microscopy and spectroscopy as well as for the generation of data that describe structure, morphology and properties of materials will be explained by an experienced team of lecturers from academia and industry with knowledge in the fields of materials science, physical and chemical materials analysis as well as mathematics and informatics.
The course is intended for individuals who wish to expand their knowledge in the field of materials development and materials characterization for process control and reliability engineering and particularly in the use of new approaches of artificial intelligence. The subjects covered in this course extend from materials science and materials analysis as well as machine learning and neuronal networks for data analysis to the current challenges in industry, particularly in process monitoring and quality assurance. Scientists, engineers and technicians working in industry – in manufacturing, process and quality control and F&E – as well as scientists and engineers from research institutes and universities, who are interested to extend their knowledge in materials characterization as well as in the use of artificial intelligence for potential applications in experimental data analysis, will benefit from this course.
Chairman of the seminar is Prof. Dr. Ehrenfried Zschech, Dresden Fraunhofer
Cluster Nanoanalysis, Germany
Questions & Contact
For questions or further informations please call the following number or send us an e-mail.
Tel.: +49 (0)69-75306 757
Fax: +49 (0)69-75306 733