Project Scope
Analyze defect, yield, and excursion data to identify trends and correlations.
Investigate selected wafer, lot, tool, and process-related yield issues.
Develop dashboards, scripts, or AI-Enabled workflows for defect analysis.
Collaborate with engineering teams to collect and validate relevant data.
Document methodologies and communicate project findings.
Learning Opportunities
Gain exposure to semiconductor defect analysis and yield engineering.
Learn statistical analysis, data visualization, and root-cause investigation methods.
Develop experience working with manufacturing and engineering datasets.
Explore Generative AI, an AI Assistant, or Agentic AI for analysis and workflow automation.
Deliverables
A completed defect or yield investigation with findings and recommendations.
An analytics dashboard, automation tool, or AI-Enabled solution.
Technical documentation describing the methodology and results.
An impact assessment covering productivity, cycle time, analysis efficiency, or investigation quality.
Impact of the Project
Improve visibility of defect and yield patterns.
Accelerate selected root-cause investigations.
Reduce repetitive analytical effort through automation.
Strengthen data-driven decision-making within Real-Time Defect Analysis.
Skillsets Required
Strong analytical and problem-solving skills.
Basic knowledge of statistics, data analysis, or semiconductor manufacturing.
Familiarity with Python, Structured Query Language, data visualization, or equivalent tools.
Interest in Generative AI, Agentic AI, or AI-Enabled engineering workflows.
Clear communication and collaboration skills.
- Course of Interest
- The ideal candidate should be pursuing a degree in Electrical Engineering, Electronics Engineering, Materials Engineering, Data Science, Computer Engineering, or a related technical field.