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

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 Masters 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

Who you are?

• Pursuing a Masters 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.
  
   
• 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: MS Research Intern position on Solving challenging 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]

(e) Previous publication record (if any)

Our research group website: https://sites.google.com/view/imec-ap-ml/home