27 days ago - req17720
Machine Learning and Physical Modeling Design Engineer
Research & development
In a nutshell
Research & development
Do you enjoy solving algorithm design problems for semiconductor metrology industry, with demanding time, accuracy, and memory requirements ? Do you like to use your creativity, your in-depth knowledge of physical and machine learning principles, and your hands-on software experience with practical problem solving, being part of a highly talented group of algorithm experts ?
Within ASML the sector Development & Engineering is responsible for the development, specification and design of new ASML products. The Business Line Applications provides integrated solutions with computational, metrology and control technology. These solutions extend and improve the performance of lithography and patterning products for the semiconductor industry.
The Algorithms and Physical Modeling group covers the development of models and methods required to infer physical model parameters from optical scatterometry data, productized with the YieldStar product. Relevant new metrics, as well as new measurement functions, with optimum performance characteristics using the raw acquisitions are identified, designed and implemented. The group secures the ASML Competence of Applied Mathematics for Parameter Estimation.
- Propose solutions for physically and statistically correct parameter inference, machine learning and optimization algorithms, and system calibrations, which address customer’s metrology needs, improve semiconductor metrology and enable control solutions.
- Communicate crystal clearly on the physical principles, algorithm solutions and mathematical models to stakeholders, without omitting the essentials.
- Be energized by creating modular code, and help colleagues in contributing to a lean and maintainable code architecture.
- 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.
Ph.D. in Physics, Applied Mathematics, Electrical Engineering, or Computer Science
- Excellence in numerically stable modeling with physically sound insights
- Affinity with driving for customer-oriented solutions, and closing the feedback loop to the algorithmic strategy and design
- Ability to explain complex physical models and algorithms in a crisp way, without omitting the essentials
- Experience in machine-learning, robust optimization or stochastic programming, and their application to physical problems
- Affinity with maintainable, modular code architecting, and clean code development
- Sound understanding of the fundamentals such as linear algebra, probability theory, (deep) learning methods
- Drive creative solutions -within the bigger picture- with the product and customer in mind
- Decisive and self-initiating in an ambiguous environment
- Able to influence without power
- Team worker
- Pragmatic approach and pro-active attitude, with result focus and a ‘can do’ spirit
Context of the position
This position is available withinthe Algorithms and Physical Modeling group. the group covers the development of models and methods required to infer physical model parameters from optical scatterometry data, productized with the YieldStar product.
Keywords: parameter inference, physical modeling, physical calibration, mathematics, optical physics, software, robust optimization, machine learning, neural network, inverse problems, classification and regression, information theory