PhD - Leuven | More than two weeks ago
The goal of this PhD research is to explore and invent/utilize data analysis methods and advanced machine learning architectures/strategies to control advanced process steps
What will you do?
As EUV based lithography gets adopted to keep scaling semiconductor devices in our chips, new metrology and inspection challenges arise. We need to measure these small dimensions fast but without losing accuracy and repeatability. Metrology and inspection are at the heart of process control. Without adequate metrology and inspection capability yields suffer. Conventional process and metrology tools, used in the industry, while generating a lot of data, do not always use them in feed-forward and feed -back cycles. However, there is a continuous need for better insight into process control by using this massive data. The sources of these data may range from tool process-logs to lab metrology to computational to FAB metrology inspection. Manual supervision, analysis and finding any relevant inter and /or intra-correlation between these monstrous data sources is nearly impossible and therefore requires better data analysis methods and advanced machine learning techniques. The goal of this project is to use data from the manufacturing tools and use them for building models for better process control and correlate with electrical performance of devices.
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 PhD
What 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?
Computer Science/Engineering, Chemistry/Chemical Engineering, Electrotechnics/Electrical Engineering, Materials Engineering, Physics. Additional coursework in Machine Learning is a plus.
Send your application by email or any questions concerning this vacancy to Dr. Sandip Halder (firstname.lastname@example.org) and Dr. Bappaditya Dey (email@example.com) indicating “Job Application: PhD position on Solving tough Semiconductor Manufacturing problems 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]
Required background: Computer Science/Engineering, Chemistry/Chemical Engineering, Electrotechnics/Electrical Engineering, Materials Engineering, Physics, Machine Learning specialization
Type of work: 20% for literature study, 20% for data collection (Clean room/FAB tools) and preparation of experiments, 60% for machine learning framework design and modeling and data analysis
Supervisor: Stefan De Gendt
Co-supervisor: Bappaditya Dey
Daily advisor: Bappaditya Dey, Sandip Halder
The reference code for this position is 2023-044. Mention this reference code on your application form.