Chair of Semiconductor Test and Reliability (STAR)

Machine Learning for Computer-Aided Design and Emerging Nanotechnologies for Beyond CMOS

The Chair of Semiconductor Test and Reliability is devoted to research in Machine Learning for Computer-Aided Design (MLCAD) with a primarily focus on design for reliability and design for testing in cutting-edge nanotechnologies. Our work covers both advanced sub-10nm technologies such as nanowires and nanosheets transistors as well as emerging technologies such as negative capacitance transistors and ferroelectric transistors for future ultra-low power circuits and memories. In addition, we develop different novel techniques to increase the reliability and efficiency of neural processing units, which are essential for all applications of artificial intelligence.

We are in close collaboration with several leading industry and universities worldwide. Our research efforts are spread across the entire computing stack starting from semiconductor device physics to digital/analog circuit design all the way up to system-level management with the key goal of investigating how machine learning methods can be employed to reshape the future of System-on-Chip (SoC) design.

For students from both Electrical Engineer and Computer Science, we offer several exciting Master and Bachelor thesis topics as well as a wide range of opportunities for research- and teaching-assistance. If you are curious and interested in how future computing looks like and which new challenges the upcoming technologies bring, please contact us.


This image shows Hussam Amrouch

Hussam Amrouch

Prof. Dr.-Ing.

Semiconductor Test and Reliability,
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