Process Engineering Intern, DFBU Productivity

About the Role

The Global Products Group is dedicated to excellence in the design and engineering of Lam’s etch and deposition products. Within it, the Dielectrics Deposition (DFBU) Productivity organization helps customers get the most from Lam’s deposition tools by raising capacity, reducing downtime, and preventing wafer scrap.

The Impact You’ll Make

You will help improve the productivity of Lam’s Dielectric Films Deposition products in customer fabs, working with a mentor and experienced engineers to diagnose issues that limit tool throughput and uptime. By summer’s end, you will own a defined project and present your results to DFBU leadership.

Example Summer Projects

Each Intern Owns One Primary Project Matched To Business Needs And Background; Ph.D. Interns Take On Larger, More Open-ended Work.

Typical Projects Include:

• Chamber recovery time reduction: A study of the steps between maintenance and return to production, with data-backed proposals to shorten recovery and the qualification wafer runs to support them.
• Preventive parts replacement: A ranked list of high-risk parts with recommended replacement intervals, built from field failure history and part-life data.
• Fleet productivity dashboard: An automated view of uptime, downtime drivers, and chamber matching across a tool fleet, with a short guide for the engineering team.

Key Responsibilities

• Run experiments on dielectric deposition tools in lab and clean-room environments, and analyze film and metrology data.
• Apply model-based and machine learning approaches to predict and prevent productivity losses.
• Analyze tool and fleet data (uptime, downtime, chamber recovery) to identify top improvement opportunities.
• Support root-cause investigations and preventive parts-replacement plans to reduce downtime and wafer scrap.
• Present findings and recommendations to engineering and leadership in a short technical memo.

Requirements & Tech Stack

• Must be an M.S. or Ph.D. student (Masters or Doctorate) in Chemistry, Physics, Electrical Engineering, Chemical Engineering, Mechanical Engineering, or Materials Science/Engineering with strong academic standing, returning to school after the internship.
• Strong foundational understanding of physical sciences (Thermodynamics, Physical chemistry).
• Strong communication skills to convey project summary and results.
• Ability to work in a clean-room environment wearing personal protective equipment, clean-room suit or smock.

Preferred Qualifications

• Background in data science or AI/machine learning based process development.
• Coursework or lab experience in thin films, CVD, or plasma processing.
• Experience with thin film analytical methods (e.g., SEM/TEM, XPS, XRD, ellipsometry).
• Familiarity with problem-solving methods such as SPC, DOE, 5-why, or 8D.
• Experience analyzing data with Python, JMP, or MATLAB.