Project description

  • Predicting solder joint reliability with machine learning approaches 
  • Improved training of AI models through material science and physics insights, specifically Physically Informed Neural Networks 
  • Inclusion of a large aging data set with TTA, SAM, in-situ stress measurements for understanding aging and training of the models
  • Material science investigation of the solders and the solder joints by shear creep tests and nanoindentation at different agings


Project information

Tasks THICreation of the dataset (aging+measurements), Development of AI models, FE-Simulation
Project partnerConti Temic Microelectronics GmbH, XITASO GmbH IT & Software Solutions, mts Consulting & Engineering GmbH, Technische Hochschule Ingolstadt
Project sponsorBayerisches Verbundforschungsprogramm Förderlinie "Digitalisierung", Bayerisches Staatsministerium für Wirtschaft
Project term09/01/2021 bis 08/31/2024



Head of Fraunhofer Application Center "Connected Mobility and Infrastructure"; Research Professor Assembly and Connection Technology
Prof. Dr. Gordon Elger
Phone: +49 841 9348-2840
Room: A114

Open positions

If you are interested in vacancies for student work within the research group, please send an email with CV to