For the manufacturing of semiconductors and integrated circuits, an increasing number of control schemes are introduced that aim to limit undesired effects on machine performance due to thermal disturbances such as temperature drifts, structural deformations and wavefront errors. To remedy these problems, advanced thermal control solutions will be increasingly important. This, because thermal disturbances become predominant as a result of increased power of light sources, increased machine throughput and more stringent performance objectives.
To support future system designs, including hardware design, sensor and actuator layout and control solutions, a number of challenges arise in this context. This PhD position is part of a larger project on hybrid modelling and sensor/actuator layout optimization for thermal control in lithography applications, funded through the High-Tech Systems and Material (HTSM) program with the Dutch research Council (NWO) and the Eindhoven Artificial Intelligence Systems Institute (EAISI).
The core challenge of this PhD position is to develop techniques and tools that provide computationally tractable and accurate dynamic models of the underlying thermo-mechanical system. The intended research focuses on understanding and modelling the tribological interaction between the wafer and wafer-supporting burls as a result of thermo-mechanical stress in the wafer. The physical nature of this system leads to first principle models that are complex and computationally inefficient due to nonlinear characteristics, spatial-temporal behavior, hysteretic phenomena and iterative computational schemes.
The purpose of the research is to develop surrogate models that allow for accurate and computationally efficient predictions of the wafer displacements and overlay errors. The PhD project envisions an investigation of the role of Artificial Neural Network (ANN) models in combination with their training rules, so as to avoid iterative computations and translate prior physical knowledge into an effective and efficient Artificial Neural Network model.
The CS group research activities span all facets of systems and control theory, such as linear, nonlinear and hybrid systems theory, model predictive control, machine learning for modelling and control, modelling and identification and formal methods in control. The CS group has a strong interconnection with other academic institutions and industry via national and European funded projects in a diverse range of application areas, often focusing on interdisciplinary research.
The PhD will join the group and interact with the other members of the CS group (around 40 researchers). Furthermore, the project is part of a larger research consortium where he/she will interact with a mix of academic and industrial research partners. Research within the CS Group is characterized by personal supervision. The PhD will have access to the advanced courses offered by the Dutch Institute for Systems and Control, and will be able to attend national and international scientific conferences.
For more detailed information on the activities of the group please check http://tue.nl/cs/
We are looking for a candidate who meets the following requirements:
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Do you recognize yourself in this profile and would you like to know more?
Please contact the hiring manager prof.dr. S. Weiland, s.weiland[at]tue.nl or have a look in our lab.
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