ASML is one of the world’s leading manufacturers of chip-making equipment.
Our vision is to enable affordable microelectronics that improve the quality of life.
To achieve this, our mission is to invent, develop, manufacture and service advanced technology for high-tech lithography, metrology and software solutions for the semiconductor industry.
ASML's guiding principle is continuing Moore's Law towards ever smaller, cheaper, more powerful and energy-efficient semiconductors. This results in increasingly powerful and capable electronics that enable the world to progress within a multitude of fields, including healthcare, technology, communications, energy, mobility, and entertainment.
ASML - Statistical Learning and Physical Model Design Engineer ( Job Description
Within D&E Applications, the group YieldStar Algorithms and Physical Modeling covers the development of physical, optical and mathematical models and methods required to infer physical model parameters from optical scatterometry data.
Relevant new metrics and algorithms, as well as new measurement functions, with optimum performance characteristics using the raw acquisitions are identified, designed and implemented.
The group secures both the Scatterometry Modeling Competency and the Applied Mathematics Competency.
Job Description
- Propose and develop new or improved parameter estimation algorithms, physical models and calibrations that enable and improve semiconductor metrology solutions beyond the optical resolution limit.
- Communicate crystal clearly on the mathematical principles, algorithm solutions and physical models to stakeholders, without omitting the essentials.
- Design and realize fully functional proof-of-concept subsystems on the edge of system specifications, costs and project planning, thereby contributing directly to products for B2B customers world-wide.
- Review technical analyses from the team, and structure team contributions keeping the overview.
- Consolidate technical-team identity in communication with other departments.
- Interfacing with the Research and on-product applications groups, while developing the best metrology solutions and a well-founded vision on semiconductor metrology.
- Contribute to technical product roadmaps and generate intellectual property protecting ASML products.
- Working as a team with similar-minded people, benefitting from each other's specific competences.
- Keywords: parameter estimation and signal processing, inverse problems, regularization, physical calibration, optimization, applied statistics, machine learning, pattern recognition and information theory.
ASML - Statistical Learning and Physical Model Design Engineer ( Job Requirements
- PhD in Electrical Engineering, Computer Science, Applied Mathematics, or Physics
Experience- Established experience in mathematical and physical modeling, (big) data analysis and algorithm design
Personal skills- Experience with machine-learning techniques, and with parametric and non-parametric Bayesian methods
- Excellence in physical modeling and code development
- Drive creative solutions -within the bigger picture- with the product and customer in mind
- Ability to explain complex physical models and algorithms in a crisp way, without omitting the essentials
- Decisive and self-initiating in an ambiguous environment
- Ability to influence without power
- Team worker
- Pragmatic approach and pro-active attitude, with result focus and a ‘can do' spirit
ASML - Statistical Learning and Physical Model Design Engineer ( Application Information
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ASML - Statistical Learning and Physical Model Design Engineer ( Summary
Education Backgrounds: |
Mathematics Micro / Nano Technology Physics |
Specialties: |
Algorithms Mathematics Modeling Research (R&D) Signal Processing
|
Education Level: |
Postgraduate (Masters) Doctorate (PH.D)
|
Experience: |
0 - 2 years 2 - 5 years 5 - 10 Years
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Languages spoken: |
English |
Job Location: |
Veldhoven, Netherlands |
Keywords: |
parameter estimation and signal processing, inverse problems, regularization, physical calibration, optimization, applied statistics, machine learning, pattern recognition and information theory. |
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