Aptm Yield Analysis/device Engineer

Intel Intel · Semiconductors · New Mexico, Albuquerque, United States

This role focuses on improving manufacturing yield and defect reduction in advanced packaging technology by extracting insights from large datasets using statistical methods and machine learning techniques. The engineer will develop solutions, influence the roadmap, and partner with various engineering teams.

What you'd actually do

  1. Extract insights from structured and unstructured data by quickly synthesizing large volumes of data, applying statistical methods and machine learning techniques.
  2. Develop solutions to problems by utilizing formal education, knowledge of manufacturing process, statistical knowledge, and problem-solving tools.
  3. Independently drive recommendations and influence the Yield improvement roadmap.
  4. Good understanding of the relationship between electrical and physical fails including a deep knowledge of FIFA, DFT, Sort/Test, Integration/Process Flow, Datamining, Databases, Data manipulation, and Data visualization.
  5. Good understanding of Inline Defect Metrology, detection capabilities and underlying defect systems in the factory.

Skills

Required

  • Bachelor's degree with 4+ years of relevant experience, OR Master's degree with 3+ years of relevant experience OR PhD degree in Materials Science and Engineering or Mechanical Engineering or Computer Science or Information Systems or Chemical Engineering or Electrical Engineering or Chemistry or Physics or any other related discipline
  • Experience listed above should be in data analysis through JMP, Python, or other data engineering software.

Nice to have

  • Understanding and hands-on application of statistical analysis.
  • Demonstrated proficiency in structured technical problem-solving.
  • Demonstrated understanding of product design/circuit/architecture as relevant for yield analysis.
  • Demonstrated understanding of inline metrology capabilities as relevant for yield analysis.

What the JD emphasized

  • statistical methods and machine learning techniques
  • large volumes of data
  • manufacturing process
  • yield improvement roadmap
  • electrical and physical fails
  • Inline Defect Metrology

Other signals

  • extract insights from structured and unstructured data
  • applying statistical methods and machine learning techniques
  • synthesizing large volumes of data
  • yield and defect improvement
  • manufacturing process