/Solving tough Semiconductor Manufacturing problems towards Advanced Process Control using Machine Learning

Solving tough Semiconductor Manufacturing problems towards Advanced Process Control using Machine Learning

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 after each year). The PhD research is fundamental and innovative, but with clear practical applications. You will join a young and enthusiastic team of  interdisciplinary researchers. The PhD position is available from January 2022.


Who you are?

  • 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.

Required background: Chemistry/Chemical Engineering, Computer Science, Electrotechnics/Electrical Engineering, Materials Engineering, Physics. Additional Basic/Advanced Knowledge in Machine Learning is a plus.

Type of work: 50% for preparation and execution of experiments, 30% for data analysis, 20% for literature study.

Supervisor: Stefan De Gendt

Co-supervisor: Sandip Halder

Daily advisor: Bappaditya Dey

The reference code for this position is 2022-129. Mention this reference code on your application form.