Process Simulation Engineering Intern, MS/PhD

About the Role

The Office of the CTO is where innovation takes center stage. As a Process Simulation Engineering Intern, you’ll help build a virtual twin of plasma etch processes. Your work will directly support data-driven process development for Lam’s etch products. This is an onsite-flex position (3+ days in the office).

Key Responsibilities

• Build and refine models of how plasma etches a surface: ion and neutral species interactions with the wafer, sticking and reaction probabilities, redeposition, and ion energy and angle effects.
• Calibrate model parameters against measured profiles (CD vs. depth from SEM/TEM images).
• Design simulation experiments (parameter sweeps, design of experiments) to generate training data.
• Build and validate fast surrogate models of the slower physics simulations, including checks on accuracy and uncertainty.
• Use surrogates to explore which settings matter most and to optimize process conditions.
• Write Python tools to automate simulation runs, data processing, and plotting.
• Document the workflow and present results at the end of the internship.

Requirements & Tech Stack

• Strong Python skills, including hands-on experience building and training models in PyTorch (or a similar framework such as JAX or TensorFlow).
• Hands-on experience building machine-learning or regression models.
• Strong analytical and communication skills, and the ability to work effectively in a team.

Qualifications

• Currently pursuing an MS or PhD in chemical engineering, physics, materials science, chemistry, or a related field.
• Background in reaction kinetics, transport phenomena, or surface/plasma science.

Preferred Qualifications

• Coursework or research in plasma physics or plasma-surface interactions.
• Experience with surrogate modeling methods: Gaussian processes, neural networks, polynomial chaos, or reduced-order models.
• Experience with GPyTorch/BoTorch, physics-informed neural networks, or uncertainty quantification.
• Experience with design of experiments, sensitivity analysis, Bayesian calibration, or optimization.
• Experience with physics-based simulation tools (finite element, Monte Carlo, or feature-scale/profile simulators).
• Familiarity with semiconductor etch processes or SEM/TEM image analysis.