About the role
AI summarisedThis internship focuses on applying data analytics, statistics, and machine learning to semiconductor photolithography processes. The intern will analyze large datasets, develop predictive models, and contribute to process optimization without direct wafer handling. The role emphasizes learning lithography fundamentals and translating data insights into process improvement recommendations.
IDMOnsiteSTPG
Key Responsibilities
- Apply photolithography fundamentals, data science, statistics, and machine learning to semiconductor process development
- Work on analytical projects involving large-scale data correlation
- Enhance recipe accuracy through data analysis
- Conduct multi-level characterization studies
- Develop modeling, simulation, and predictive analytics for process improvement
- Gain exposure to photolithography concepts including immersion optics, polarized illumination, low-k1 imaging, and material behavior
- Learn how lithography chemical processes and materials differ across 365 nm, 248 nm, and 193 nm technologies
- Work with lithography simulation tools for low-k1 regime applications
- Analyze large semiconductor datasets to identify trends, correlations, and process sensitivities
- Apply statistical analysis, SPC concepts, and Six Sigma methodologies to evaluate process performance
- Develop predictive models using machine learning or advanced analytics frameworks
Requirements
- Understanding of optical principles such as immersion lithography, polarized illumination, and low-k1 imaging
- Familiarity with lithography chemicals and materials across 365/248/193 nm exposure technologies
- Experience using lithography simulation tools for low-k1 applications
- Knowledge of material properties and characterization relevant to lithography
- Strong foundation in statistics, probability, and data analysis (Cpk, distributions, hypothesis testing)
- Familiarity with machine learning or data science tools (Python, R, or similar)
- Understanding of SPC and Six Sigma methodologies
- Ability to interpret complex datasets and communicate insights clearly
- Background in Engineering (Electrical, Mechanical, Chemical), Data Science, Statistics, Physics, or Semiconductor Engineering