Thomas Schütte¹, Jan-Peter Urbach¹, Fabian Neuhaus², Martin Glauer², Stephan Barth³, Christian Käpplinger^4
1PLASUS GmbH, Mering, Germany
²Otto von Guericke University of Magdeburg, Germany
³Fraunhofer Institute for Electron Beam and Plasma Technology FEP, Dresden, Germany
⁴PVA TePla Analytical Systems GmbH, Jena, Germany
The recent progress of large language models (LLMs) such as ChatGPT or DeepSeek has generated a lot of interest for the prospect of artificial intelligence in the field of industrial process control. LLMs and other approaches based on deep learning architectures rely on very large data sets that are used train the models.
In the industrial context generating comprehensive and validated data points usually requires considerable effort in terms of time and money. A single data point will require to process at least one sample in the process of interest and perform a number of measurements and tests afterwards to characterize the performance of the product. Process parameters from the respective run have to be stored together with the results of the characterization results in a machine-readable format. The bottleneck is most often the necessary amount of characterization measurements that may not be part of the usual quality assurance routine.
So, a major challenge to apply artificial intelligence (AI) methods for industrial process control is to extract meaningful AI models based on data sets of limited size. In this talk we will present strategies for this challenge that are developed in the context of the project “Digitalization of materials research on thin-film materials using the example of high-resolution piezoelectric ultrasonic sensors” (DigiMatUs) which is part of the German research initiative “MaterialDigital”. In this project, reactive magnetron sputtering of AlScN layers for the use in ultrasonic transducers is investigated by means of machine learning. Strategies to cope with the limited amount of data include the use of in-situ diagnostic techniques such as plasma monitoring or in-situ reflectometry, the recourse to physical quantities and the introduction of expert knowledge.