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Implementation of AI clustering approach to help the analysis of nano-scale transistor behaviour
Leuven | More than two weeks ago
When carrying out device testing, several hundred curves are produced and have to be evaluated in terms of whether they behave as expected from the laws of physics. To facilitate this process, you will contribute to write a python-based program to find clusters (commonalities) in curves leveraging DLA (Deep Learning Algorithms).
The transistor is an essential pillar of modem CMOS-based circuits. As device geometries reduce drastically, it becomes more and more difficult to do accurate modeling of the transistor and parasitic effects.The goal of this thesis is twofold. First, the candidate will review the current models used in the field of nano-scale transistors and will define quantitative parameters to ensure the correct modelling of the transistors following well known physics laws. Secondly, the main work will be more practical view and will focus on the development of python scripts enabling automatic data clustering based on classication -oriented machine learning algorithms.
Type of project: Combination of internship and thesis
Duration: 9 months
Required degree: Master of Science
Required background: Computer Science
Supervising scientist(s): For further information or for application, please contact: Murat Kocak (Murat.Kocak@imec.be) and Jerome Mitard (Jerome.Mitard@imec.be)
Only for self-supporting students.