Intern - ADTS PEE Dry Etch

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 provides 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.
• Final internship project presentation to department leadership.

Impact of Project

• Improve the understanding and control of cumulative error variation.
• Enhance inline process stability and contribute to device yield performance.
• Identify 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.