Leuven | More than two weeks ago
The goal of this research topic is to explore and apply advanced deep learning algorithms and architectures for solving specific problems in E-Beam metrology and Inspection, more specifically in SEM image analysis.
Scanning Electron Microscope (SEM) are used widely in the semiconductor industry for metrology and inspection. Among the various types of SEMs, CD-SEMs are of profound importance mainly because they measure the CD (critical dimension) of the circuit patterns based on which the entire litho process is targeted. Review-SEMs and E-beam inspection tools are gaining importance as we shrink to N3 nodes and below because of their high resolution. However, as circuit patterns become smaller (pitches less than 32 nm) the extraction of repeatable and accurate defect locations along with CD metrology becomes significantly complicated especially post ADI (After Develop inspection). This is simply because at these pitches the number of pixels available to detect a defect or do metrology is approaching single digit numbers.
The goal of this research topic is to explore and apply advanced deep learning algorithms and architectures in computer vision domain for solving specific problems in E-Beam metrology and Inspection, more specifically for SEM image analysis.
The student will learn conventional process flow and 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:
Type of work: 50% for preparation and execution of experiments, 30% for data analysis, 20% for literature study.
Type of project: Combination of internship and thesis, Thesis.
Duration: 6 months. Can be extended based on performance.
Required degree: Master of Engineering Technology, Master of Science, Master of Engineering Science
Required background: Computer Science/Engineering, Electrotechnics/Electrical Engineering, Physics, Machine Learning/Artificial Intelligence.
Supervising scientist(s): For further information or for application, please contact: Sandip Halder (Sandip.Halder@imec.be) and Bappaditya Dey (Bappaditya.Dey@imec.be)
Our group website: https://sites.google.com/view/imec-ap-ml/home
Imec allowance will be provided.
Type of project: Combination of internship and thesis, Thesis
Duration: 6 months. Can be extended based on performance.
Required degree: Master of Engineering Technology, Master of Engineering Science, Master of Science
Required background: Computer Science, Electrotechnics/Electrical Engineering, Physics, Other
Supervising scientist(s): For further information or for application, please contact: Bappaditya Dey (Bappaditya.Dey@imec.be) and Sandip Halder (Sandip.Halder@imec.be)
Imec allowance will be provided.