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.  

Tasks

• Study of the literature of modeling, artificial neural networks, machine learning, and relevant physical and system-theoretic properties of the wafer-support system.
• Development of data-driven learning and identification techniques and algorithms for the training of neural networks and/or identification of physical system parameters.
• Development of techniques to infer data-driven surrogate models that can be adjusted on the basis of use cases.
• Analysis of consistency, computational efficiency and convergence of these techniques.
• Empirical validation of model quality in terms of accuracy and predictability of thermal disturbances and overlay errors.
• Dissemination of the results of your research in international and peer-reviewed journals and conferences.
• Writing a successful dissertation based on the developed research and defending it.
• Assume educational tasks like the supervision of Master students and internships.

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:

• You are a talented and enthusiastic young researcher.
• You have experience with or a background in systems and control, mathematics, physics,  signal processing, machine learning, data-driven modelling.
• Preferably you finished a master's in Systems and Control,  Mechanical or Electrical Engineering, (Applied) Physics or (Applied) Mathematics.
• You work well in a team with mixed expertise, with an interest towards technical applications.
• You have good programming skills and experience.
• You have good communicative skills and a cooperative attitude in the work of a research team.
• You are creative and ambitious, hard-working, and persistent.
• You have good command of the English language (knowledge of Dutch is not required).

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:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale 27 (min. €2,541 max. €3,247).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.