12 days ago - req16046

Machine Learning and Physical Modeling Design Engineer

Research & development

Physics

In a nutshell

Location

Veldhoven, Netherlands

Team

Research & development

Experience

0-2 years

Degree

PhD

Job Category

Physics

Travel

10%

Introduction

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.

Job Mission

Within D&E Applications, the metrology group On Product PerformanceAlgorithms and Physical Modeling covers the development of models and methods required to infer physical parameters from optical scatterometry data. 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 Competency of Applied Mathematics for Parameter Estimation.

Job Description

  • Propose solutions for statistically correct parameter inference, machine learning and optimization algorithms, and system calibrations, which 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.

Education

Ph.D. in Physics, Applied Mathematics, Electrical Engineering, or Computer Science

Experience

  • Excellence in numerically stable modeling with physically sound insights
  • 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

Personal skills

  • 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

The position is available within the metrology group On Product PerformanceAlgorithms and Physical Modeling. The groupcovers the development of models and methods required to infer physical parameters from optical scatterometry data.

Keywords: parameter inference, physical modeling, physical calibration, mathematics, software, robust optimization, machine learning, neural network, Kriging, Bayes, inverse problems, surrogate modeling, classification and regression, information theory