AI for Low Energy Electron Microscopy
Project Description
We will explore the use of artificial intelligence (AI) to interpret images of crystalline sample surfaces obtained using low energy electron microscopy (LEEM). AI training will be done using LEEM images simulated using Fourier optics (FO) or Contrast Transfer Function (CTF) theory. The FO or CTF simulations will be performed using programs that run on MATLAB or Python programs.
Supervisor
ALTMAN, Michael Scott
Quota
2
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles
The student will learn rudimentary aspects of FO and CTF theory before using existing programs to generate image simulations of many objects that produce LEEM image phase contrast. The student will use these data to train AI. Then, AI will be developed to evaluate image contrast features produced by random test objects. The resulting work may also be used to evaluate actual experimental data.
Applicant's Learning Objectives
Develop an understanding of scientific programming
Develop an understanding of applications of AI in image recognition
Develop and understanding of image formation theory in microscopy
Develop an understanding of applications of AI in image recognition
Develop and understanding of image formation theory in microscopy
Complexity of the project
Challenging