Statistical Optimization and Calibration of Lithography Models
Develop advanced statistical methods to optimize and calibrate lithography models for high-density microelectronic devices.
Best for:Best for candidates with a strong background in numerical analysis, statistical modeling, or computational methods, interested in microelectronics and photonics research.
Overview
This PhD position at Université Grenoble Alpes focuses on advancing statistical techniques to optimize and calibrate lithography models used in generating photomask designs for high-density microelectronic circuits. The work will address challenges in optical proximity correction (OPC) by improving model calibration efficiency, reducing data acquisition bottlenecks, and mitigating variability in experimental data. The project is hosted at the Laboratoire de Patterning Computationnel within the Département des Plateformes Technologiques (LETI), a leading research hub in microelectronics and photonics. Applicants will contribute to cutting-edge research in numerical analysis and photonics, addressing critical fabrication challenges in semiconductor manufacturing.
What you'll do
- Develop innovative statistical methods to optimize input data for lithography model calibration.
- Design and implement algorithmic improvements to streamline the model calibration workflow.
- Analyze the impact of experimental data variability on lithography model accuracy and reliability.
- Collaborate with experimental teams to refine test patterns and data acquisition processes.
- Publish findings in peer-reviewed journals and present research at international conferences.
- Contribute to the advancement of semiconductor fabrication techniques for high-density circuits.
Requirements
Degree required: Master's degree (or equivalent) in Engineering, Physics, Applied Mathematics, Computer Science, or a related quantitative field
Degree offered: PhD
Language: English proficiency (no specific test score required)
Instruction: French (for administrative purposes) and English (for research)
- Strong background in numerical analysis or statistical modeling
- Experience with computational methods or algorithmic optimization
- Familiarity with semiconductor fabrication processes or lithography techniques
- Proficiency in programming languages such as Python, MATLAB, or C++
- Ability to work with large datasets and perform data analysis
- No specific nationality or residency restrictions mentioned
- Open to applicants with relevant academic background and research experience
Funding & terms
How to apply
Deadline: 2026-10-31
Applicants must submit their application through the Campus France portal. Required documents include a CV, a cover letter, academic transcripts, and a research proposal. Additional documents may be requested based on the evaluation of the initial application.
- CV
- Cover letter
- Academic transcripts (BSc + MSc)
- Research proposal
- Proof of English proficiency (if applicable)
Contact
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