Computer Vision for Defect Inspection and Metrology: Solving Semiconductor Manufacturing Challenges towards Advanced Process Control using Machine Learning

What will you do? 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 Master’s internship 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 (labelled) 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 selected intern/student will learn conventional process flow and will be responsible to work collaboratively towards 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 and address or overcome metrology/process control-based tool limitations
5. Collaboration on patent/publications and presentations at international conferences/high-indexed journals.
What we do for you?? We offer a fully funded internship scholarship (subject to conditions) for a maximum duration of 9 months, contingent on satisfactory progress. The internship focuses on fundamental and innovative research with clear practical applications. You will join a young and enthusiastic team of researchers and domain experts. The Master’s internship position is available immediately.
Who you are? ✓ You hold at least a Bachelor’s degree or are currently pursuing a Master’s degree at a reputable university in Engineering/Science, preferably in one of the following fields:
restricted
• 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 are able to work independently, demonstrate a strong sense of responsibility, and adhere to timelines and milestones defined by ongoing research projects and conferences. You should be genuinely motivated by the research topic and committed to completing a research thesis as part of your university degree program. ✓ 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 internship to Bappaditya Dey ([email protected]) indicating “MS Thesis Application: Computer Vision for Defect Inspection and Metrology: Solving Semiconductor Manufacturing Challenges towards Advanced Process Control using Machine Learning” in the subject.
Applications should include:
(a) SoP (clearly indicating both your research background/interest as well as any specific research skills)
(b) your professional Resume [Max 2 page]
(c) latest degree transcript
(d) GRE/TOEFL score [preferable but not mandatory]
(e) Previous publication record (if any)
Our research group website: https://sites.google.com/view/imec-ap-ml/home Type of internship : PhD internship, Master internship, Bachelor internship Duration : 6 months, can be extended 9 months Required educational background : Computer Science, Physics University promotor : Roel Wuyts (KULeuven) Supervising scientist(s) : For further information or for application, please contact Bappaditya Dey ( [email protected] ) The reference code for this position is 2026-INT-315 . Mention this reference code in your application. Imec allowance will be provided for students studying at a non-Belgian university. Applications should include the following information: resume motivation current study Incomplete applications will not be considered