Thin-Film Process Engineer Intern – DOE & AI-Driven

TOKYO ELECTRON AMERICA, INC.; San Jose (CA); On-site; USD 28,000 - 41,000; Part time

Benefits offered by this job

Educational reimbursement

Job summary

TOKYO ELECTRON AMERICA, INC. TEL Silicon Valley Thin Films development team offers a Summer 2027 Internship in San Jose, CA. You will work on meaningful projects applying Lean thinking and data-driven methods to improve process performance and yield.

The role targets students in Physics, Materials Science, Mechanical, Electrical, or Chemical Engineering with experience in DOE, CAD, and plasma/fluids simulations. CAD tools and machine learning techniques are highly valued in this R&D setting.

Qualifications

• Graduate level (M.S. or Ph.D.) preferred; rising Junior or Senior considered for Bachelor’s level in Physics, Materials Science, Mechanical, Electrical, or Chemical Engineering.
• Knowledge of DOE, statistical methods, CAD, and computational plasma/fluids tools.

Responsibilities

• Design and conduct process DOEs for process optimization.
• Analysis and data assimilation of tool signal data and metrology results.
• Supervised work on plasma diagnostics and integration of diagnostic signals with tool and metrology data.
• Construct ROMs of chamber hardware and validate with tool signal data.
• Simulate EM fields and plasmas using commercial codes.
• Supervised work in TEL lab including sample preparation and data collection.

Skills

DOE knowledge

Statistical methods

CAD

Plasma/fluids simulation

Coding and ML/AI

Lean thinking

Education

MS or PhD preferred

Bachelor’s in Physics/Materials/Mechanical/Electrical/Chemical Eng

Tools

CAD tools

Commercial simulation codes

Job description

TOKYO ELECTRON AMERICA, INC. TEL Silicon Valley Thin Films development team offers a Summer 2027 Internship in San Jose, CA. You will work on meaningful projects applying Lean thinking and data-driven methods to improve process performance and yield.

The role targets students in Physics, Materials Science, Mechanical, Electrical, or Chemical Engineering with experience in DOE, CAD, and plasma/fluids simulations. CAD tools and machine learning techniques are highly valued in this R&D setting.