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Computer Vision for Defect Inspection and Metrology: Solving Semiconductor Manufacturing Challenges towards Advanced Process Control using Machine Learning
PhD - Leuven | Just now
This PhD project addresses fundamental computer vision challenges arising from this domain, with the semiconductor process flow providing the boundary conditions and practical constraints.
As semiconductor devices continue to scale with the adoption of high-NA (Numerical Aperture) Extreme Ultraviolet (EUV) lithography, defect inspection and metrology face unprecedented challenges. Identifying and measuring nanoscale defects requires methods that are both highly accurate and computationally efficient, yet existing inspection pipelines struggle to handle the massive, heterogeneous, and often imbalanced datasets produced in advanced manufacturing. Manual inspection and correlation of such data sources are infeasible, motivating the need for new computer vision and machine learning techniques.
This PhD project addresses fundamental computer vision challenges arising from this domain, with the semiconductor process flow providing the boundary conditions and practical constraints. The scientific focus includes:
• Unsupervised and few-shot learning for defect detection, enabling robust identification of rare or novel defect types with minimal labeled data.
• Multi-modal and context-aware representation learning, integrating metrology images with tool logs and process data to improve defect localization and classification.
• Uncertainty-aware models that quantify confidence in inspection results, supporting reliable process control.
• Scalable and efficient vision algorithms that balance high accuracy with reduced computational and cycle-time costs.
By tackling these challenges, the project aims to advance the state-of-the-art in computer vision for high-precision defect inspection and metrology, with contributions that generalize beyond semiconductor manufacturing to other domains where anomaly detection under strict constraints is critical.
The PhD student accepted for this position will learn conventional process flow and will be responsible to work collaboratively toward developing and applying “Machine learning" based optimization algorithms with a goal to tackle the aforementioned challenges in terms of 1) Reducing computational cost, 2) reduce tool cycle time, 3) predictive process control approach in enabling advanced node semiconductor manufacturing. 4) Improving metrology data.
Machine learning applicability includes:
1. Brainstorm “Technical diligence” of the project: to meet desired performance and engineering timeline.
2. Tool Data Analysis: Collect data, analyse data, and suggest hypothesis with expertise feedback loop.
3. Image Processing applicability: Collect Image data (SEM/TEM/EDR/..), suggest ML based hypothesis to extract improved SEM based measurements.
4. Machine Learning Modelling – build from scratch or improving an existing algorithm for a given application task optimization o address or overcome metrology/process control-based tool limitations
5. Collaboration on patent/publications and presentations at international conferences/high-indexed journals
6. Supervision of master theses related to the subject of this PhDWhat we do for you?
We offer a fully funded PhD scholarship for a maximal period of 4 years (upon positive progress evaluation). The PhD research is fundamental and innovative, but with clear practical applications. You will join a young and enthusiastic team of researchers, post-docs and professors. The PhD position is immediately available.
Who you are?
✓ Holder of a Master’s degree in Engineering/Science, preferably in one of the following:
Computer Science/Engineering, Electrotechnics/Electrical Engineering, Physics, Artificial Intelligence and Machine Learning (or any interdisciplinary course). Additional coursework in Machine Learning is a plus.
✓You work independently, have a strong feeling of responsibility, and be committed to timing and milestones set forward by different research projects/conferences. Must be motivated by the research topic as well as obtaining a PhD degree.
✓You have a strong research interest in investigating different advanced machine learning architectures and algorithms appropriate in the context of specified problem domain as well as eagerness to advance the state-of-the-art. Knowledge of diverse framework [Tensorflow/PyTorch etc.] is a plus.
✓Strong Programming skills (Python, C, C++, skill etc.).
✓You must be visionary and with a multi-disciplinary attitude.
✓You have excellent analytical skills to interpret the obtained research results.
✓You are a team player and have strong communication skills.
✓Your English is fluent, both speaking and writing.
Interested?
Send your application by email or any questions concerning this vacancy to Bappaditya Dey (bappaditya.dey@imec.be) indicating “Job Application: PhD position on Computer Vision for Defect Inspection and Metrology: Solving Semiconductor Manufacturing Challenges towards Advanced Process Control using Machine Learning” in the subject.Required background: Computer Science, Computer Engineering, AI, Machine learning, Physics
Type of work: 60% modeling/simulation, 15% literature study, 25% experimental
Supervisor: Roel Wuyts
Co-supervisor: Bappaditya Dey
Daily advisor: Bappaditya Dey
The reference code for this position is 2026-202. Mention this reference code on your application form.