/Applying Machine Learning Techniques to solve Specific Problems in Lithography

Applying Machine Learning Techniques to solve Specific Problems in Lithography

Leuven | More than two weeks ago

The goal of this project research is to explore and invent/utilize different data analysis methods and advanced machine learning architectures/strategies to control advanced process steps during semiconductor manufacturing , more specifically in Lithography

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 research is to use data from the manufacturing tools and use them for building advanced machine learning models for better process control and correlate with electrical performance of devices.
The student accepted for this position 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 o address or overcome metrology/process control-based tool limitations
  5. Collaboration on patent/publications and presentations at international conferences/high-indexed journals
Machine learning


Type of project: Combination of internship and thesis, Internship, 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, Electrotechnics/Electrical Engineering, Chemistry/Chemical Engineering, Physics

Supervising scientist(s): For further information or for application, please contact: Sandip Halder (Sandip.Halder@imec.be) and Bappaditya Dey (Bappaditya.Dey@imec.be)

Imec allowance will be provided.

Who we are
Accept marketing-cookies to view this content.
Cookie settings
imec's cleanroom
Accept marketing-cookies to view this content.
Cookie settings

Related jobs

Area-selective deposition mechanisms when pattern dimensions reach the nanoscale

You will gain fundamental understanding that is essential to develop new processes for area-selective deposition, a bottom-up technique for creating future Logic and Memory devices.

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

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

Metal epitaxy for ultimate low-resistivity conductors in advanced microelectronic devices

Study ultimate conductor materials

Unraveling the geometry of three-dimensional devices with Raman spectroscopy

When photons and phonons meet to improve our nanoscale vision

Design of Nanostructured Arrays for Ultrafast Surface-Enhanced Spectroscopies of EUV Photoresists

Design, build (using compatible CMOS fabrication processes), and possibly (time-allowing) test nanoengineered substrates for plasmonic enhanced spectroscopic signals in sub-picosecond time-resolved near-to-mid IR absorption spectroscopies of current and next-gen EUV photoresists.

High resolution volumetric lithography of complex 3D polymeric and biomaterial structures

With the imec team and university collaborators, the student explores, selects, designs, builds, and tests workable imec-based solutions for bioprinting and printing biophotonics using projection imaging and holography fused with super-resolution imaging chemistry. 
Job opportunities

Send this job to your email