Data Analytics Intern: Dry Etch Process Optimization

Micron Technology, Inc; Singapore; On-site; SGD 20,000 - 27,000; Full time

Job summary

Micron Technology, Inc. invites motivated students to join as full-time interns dedicated to reducing cumulative error variation in staircase and chop etch processes.

You will analyze measurement data, build data-driven control strategies, and collaborate with cross-functional teams to suggest improvements that enhance inline stability and device yield. Ideal candidates are pursuing degrees in relevant engineering fields, have strong analytical skills, and familiarity with statistical tools and

Qualifications

• Strong analytical and problem-solving skills.
• Interest in semiconductor manufacturing and dry etch processes.
• Familiarity with statistical analysis and data visualization.
• Familiarity with machine learning, AI tools, or AI-enabled workflows.
• Experience with Python, R, SQL, JMP, or related data analysis tools would be advantageous.

Responsibilities

• Analyze manufacturing process and measurement data to identify sources of cumulative error variation.
• Develop data-driven control strategies and process improvement recommendations to reduce variation and improve inline process stability.
• Collaborate with cross-functional engineering teams and subject matter experts.
• Deliverables include data visualizations and proposed control strategies.

Skills

Analytical thinking

Statistical analysis

Data visualization

Machine learning / AI tools

Python / R / SQL / JMP

Education

Pursuing a degree in Materials Science and Engineering, Chemical Engineering, Electrical or Electronic Engineering, Mechanical Engineering, or a related field

Tools

Python

R

SQL

JMP

Job description

Micron Technology, Inc. invites motivated students to join as full-time interns dedicated to reducing cumulative error variation in staircase and chop etch processes.

You will analyze measurement data, build data-driven control strategies, and collaborate with cross-functional teams to suggest improvements that enhance inline stability and device yield. Ideal candidates are pursuing degrees in relevant engineering fields, have strong analytical skills, and familiarity with statistical tools and