/Processing of 2D transition metal dichalcogenide (MX2) images acquired by transmission electron microscopy

Processing of 2D transition metal dichalcogenide (MX2) images acquired by transmission electron microscopy

Leuven | More than two weeks ago

Explore image processing and machine learning data analysis to help developing 2D-materials for future generation transistors
Keywords image processing, machine learning, 2D materials, TEM

 

Two-dimensional transition metal dichalcogenides such as MoS2 have gained significant interest in the past decade for applications in future generation nanoelectronics, particularly as channel material in transistors, and optoelectronic devices. Electrical and optical properties of these devices are highly affected by the presence of defects at atomic scale. Scanning Transmission Electron Microscopy (STEM) based techniques are the most suitable to provide direct information on crystal structure and local defects. One of the main challenges with these techniques is the interpretation of the image.  A procedure for automatically recognizing and quantifying defects in these materials starting from STEM images is needed.

 

You can contribute to this challenge by applying image processing and/or machine learning aided data analysis helping to develop image processing methodology to

  • reduce artefacts in STEM images caused by the imaging acquisition or impurities
  • automatically extract information of defects and grain structure of MX2 films from high-resolution STEM images.

 

 

Requirements Python and/or MATLAB programming needed, basic knowledge in image processing and machine learning desirable, familiarity with transmission electron microscopy and crystallography a plus

 




Type of project: Combination of internship and thesis, Thesis

Duration: 6 months

Required degree: Master of Engineering Technology, Master of Science, Master of Engineering Science

Required background: Nanoscience & Nanotechnology, Physics, Materials Engineering, Chemistry/Chemical Engineering, Computer Science

Supervising scientist(s): For further information or for application, please contact: Jan Sijbers (Jan.Sijbers@imec.be) and Paola Favia (Paola.Favia@imec.be)

Only for self-supporting students.

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