Process Engineering Intern

We are seeking a motivated intern to support advanced metrology development activities focused on improving measurement accuracy and modeling robustness across key characterization techniques. This role is ideal for candidates interested in semiconductor process characterization, data analysis, and design of experiments (DOE) methodology.

What you will be working on

The intern will work with the Process Engineering and Metrology teams to:

1. Transmission Electron Microscopy (TEM) Variability & Error Modeling
•    Characterize sample to sample and region to region variability in TEM measurements.
•    Quantify measurement uncertainty and establish statistically meaningful error bars.
•    Translate TEM measurement error into corresponding DOE model error.
•    Assess how measurement uncertainty impacts p values, model significance, and predictive validity.
•    Propose methods to reduce overall model noise—either through measurement improvements or smarter experimental design.

2. Ellipsometry Modeling and Material Optical Properties (n, k)
•    Support extraction and optimization of refractive index (n) and extinction coefficient (k) for new films.
•    Analyze how ellipsometry model assumptions impact derived film thickness and composition.
•    Investigate thickness dependent behavior: identify thresholds where goodness of fit (GoF) degrades as thickness increases.
•    Evaluate breakdown mechanisms (e.g., over simplified dispersion models, depolarization effects, parameter cross correlation).

3. DOE Data Integration & Improvement
•    Combine metrology data streams (TEM, ellipsometry) to build more realistic input distributions for DOE.
•    Quantify how measurement error propagates into model confidence levels and sensitivity indices.
•    Recommend approaches to improve DOE outcomes (e.g., reduced uncertainty, alternative sampling plans, model weighting).

4. Deliverables
By the end of the internship, the student is expected to deliver:
•    A quantified error propagation framework linking key metrology tools to DOE output quality.
•    A statistical report detailing major sources of measurement uncertainty and recommended mitigation paths.
•    Updated ellipsometry models (n, k, dispersion) or parameter sets that improve GoF for thick films.
•    A final technical presentation summarizing findings, insights, and process improvement opportunities.

What we are looking for

•   Working towards his doctoral degree with focus on Physics, chemistry or material science. Other engineering disciplines might be considered.