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Tier-1 brand and metro location increase applicants, but niche computational physics skillset reduces density.
Highly domain-specific semiconductor process and computational physics expertise limits cross-industry transferability.
PhD requirement, domain expertise, and specialist scientific software skills will produce strict shortlisting filters.
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Develop and apply computational models to predict semiconductor wafer performance based on process recipes, including repeatability, uniformity, and material profile.
Use modeling to innovate and troubleshoot semiconductor processes, chamber designs, chemistries, and sequences in production and development.
Develop and maintain scientific software for semiconductor process modeling, including algorithm development, bug fixes, and feature implementations.
PhD in physics, electrical engineering, chemistry, or related field, or equivalent education and experience.
Proficiency in programming languages such as C/C++, Fortran77/90, Python, or R and experience with scientific software development best practices.
Knowledge of semiconductor processes (etch, deposition, epitaxy, electroplating, doping, etc.) and surface chemistry, plasma and reactive flow processes.
Work Experience Required: Not explicitly mentioned in the JD.
Experience with computational physics modeling techniques (kinetic Monte Carlo, molecular dynamics, particle-in-cell, direct simulation Monte Carlo).
Familiarity with machine learning/AI libraries in Python or R, and generative AI tools (Claude, Copilot, Gemini, ChatGPT).
Ability to collaborate across hardware engineering, process engineering, and program management teams to drive process innovation and troubleshooting.