Intern - ADTS PEE Dry Etch

1100 Micron SemiAsiaOP Pte Ltd; Singapore; On-site; SGD 17,000 - 20,000; Full time

Job summary

Micron Technology, Inc. in Singapore invites students to join a full-time internship on the Cumulative Error Variation Reduction in Staircase and Chop Etch Processes project.

You will analyze manufacturing process and measurement data to identify sources of cumulative error variation and develop data-driven control strategies. The role offers exposure to dry etch processes, staircase flow integration, statistical analysis, data visualization, and AI-enabled workflows, with collaboration across

Qualifications

• Strong analytical and problem-solving skills.
• Familiarity with statistical analysis and data visualization.
• Experience with Python, R, SQL, or JMP is advantageous.

Responsibilities

• Analyze manufacturing process and measurement data to identify sources of cumulative error variation.
• Develop data-driven control strategies to reduce variation and improve inline process stability.
• Create data visualizations and summaries of key findings and relationships.
• Collaborate with cross-functional engineering teams and stakeholders to implement improvements.

Skills

Analytical skills

Problem solving

Semiconductor processes interest

Statistical analysis

Data visualization

Machine learning / AI tools

Python

R

SQL

JMP

Education

Pursuing degree in Materials Science & Eng or Chemical Eng or Electrical/Electronic Eng or Mechanical Eng

Tools

Python

R

SQL

JMP

Job description

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.

Project Title

Cumulative Error Variation Reduction in Staircase and Chop Etch Processes

Project Description

Analyze manufacturing process and measurement data to identify sources of cumulative error variation within advanced-technology staircase and chop etch processes. Develop data-driven control strategies and process improvement recommendations to reduce variation and improve inline process stability. The project will provide exposure to dry etch processes, staircase flow integration, statistical analysis, data visualization, and the etching mechanisms relevant to semiconductor process control.

Project Scope

• Understand the measurement methodology and process mechanisms used to define cumulative error within the staircase formation loop.
• Analyze process and measurement data to identify factors contributing to cumulative error variation.
• Apply statistical analysis and data visualization techniques to evaluate relationships between process conditions and cumulative error performance.
• Develop potential control strategies to reduce cumulative error variation.
• Recommend opportunities to improve process margins and inline stability.

Learning Opportunities

• Gain hands‑on experience in semiconductor process data analysis and troubleshooting.
• Develop a deeper understanding of dry etch processes, staircase flow integration, and lateral and vertical etching mechanisms.
• Apply statistical analysis, data visualization, and AI‑enabled workflows to an engineering project.
• Collaborate with cross‑functional engineering teams and subject matter experts.
• Develop technical storytelling and presentation skills.

Deliverables

• Source‑of‑variation analysis for cumulative error within the staircase formation loop.
• Data visualizations summarizing key findings and process relationships.
• At least one proposed control strategy or process improvement idea.
• Recommendations for improving process margins and inline stability.

Impact of Project

• Improve the understanding and control of cumulative error variation.
• Enhance inline process stability and contribute to device yield performance.
• Identify potential opportunities to improve dry etch process efficiency and equipment productivity.

Skillsets Required

• 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.

Course of Interest

The ideal candidate should be currently pursuing a degree in Materials Science and Engineering, Chemical Engineering, Electrical or Electronic Engineering, Mechanical Engineering, or a related field.

Duration

The ideal candidate should be able to commit to a full‑time internship period of at least five months.

About Micron Technology, Inc.

We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience. To learn more, please visit micron.com/careers.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.

Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.

AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.