DATE Save the Date 17 to 19 April 2023

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Graduate School Intelligent Methods for Test and Reliability

Project Acronym
Project Type
Graduate School at the University of Stuttgart funded by Advantest
Project Dates
Contact Person
Prof. Dirk Pflueger (coordinator), Prof. Hussam Amrouch

Project Partners

  • Advantest Europe, Boeblingen
  • Institute of Computer Architecture and Computer Engineering, University of Stuttgart
  • Institute of Information Security, University of Stuttgart
  • Institute for Parallel and Distributed Systems, University of Stuttgart
  • Institute of Robust Power Semiconductor Systems, University of Stuttgart
  • Institute of Signal Processing and System Theory, University of Stuttgart
  • Institute of Smart Sensors, University of Stuttgart
  • Institute of Software Technology, University of Stuttgart
  • Institute for Visualization and Interactive Systems, University of Stuttgart
  • Visualization Research Center (VISUS), University of Stuttgart


Test methods are a main pillar of the semiconductor technology and are an essential prerequisite for obtaining high-quality products at reasonable cost. To give a major impetus on applied semiconductor test and reliability research, Advantest, a major automatic test equipment (ATE) vendor, and the University of Stuttgart jointly established a Graduate School on “Intelligent Methods for Semiconductor Test and Reliability” (GS-IMTR).

1. What makes the project concept unique?

For the first time, a company funds a multi-partner Graduate School within a University, thus supporting ten PhD candidates and one Assistant Professor with projects selected using a competitive peer-review-based process. This brings together focused interdisciplinary expertise in fields as diverse as machine learning; visualization; security; radio-frequency circuit design; emerging technologies and architectures.

2. What project outcomes can be of use to the DATE 2022 community?

The overarching question of GS-IMTR is how to leverage data generated across the whole semiconductor value chain: during post-silicon validation, test and in-field operation. Notable expected contributions are in deep learning and visual analytics for test data; system-level test; software test suite optimization; secure testing; millimeter-wave radio frequency design; and testing of emerging technologies.

3. What inputs (solutions) are expected from the DATE 2022 community?

GS-IMTR is interested in both: new problems to which its methodologies are applicable, and specific use cases for the solutions established. GS-IMTR includes funds to facilitate international collaboration that can be used for pursuing specific scientific ideas.

4. What new research topics and trends the project introduces?

GS-IMTR follows a radically interdisciplinary approach to semiconductor testing that goes beyond its current state of the art. For example, topics such as visualization of test data or privacy-preserving test applications have not received attention so far.