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Techniques for enhancing electron microscopy

Author(s)
Agarwal, Akshay.
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Karl K. Berggren.
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Electron microscopy is a powerful imaging technique that allows us to push the limits of our understanding of materials at the nanoscale. An important limitation in the application of electron microscopy to organic and biological materials is sample damage induced by the electron beam. Recently, quantum mechanical and adaptive illumination imaging schemes have been devised to use the available electron dose efficiently to get the maximum information about the specimen. The primary requirement for the implementation of these schemes is efficient illumination and detection of electrons in the microscopes, which has limited the applicability of such low-dose imaging techniques. In this thesis, we have developed and implemented low-dose imaging schemes achievable with current technology on a wide range of electron microscopes. We have also proposed microscopy schemes that combine ideas from quantum mechanical and adaptive illumination imaging to lower the electron dose required for imaging by up to an order of magnitude. Further, we have developed electron count imaging on a scanning electron microscope (SEM) and demonstrated improvement of up to 30% in image quality for the same imaging dose. Finally, we have implemented an adaptive illumination scheme on the SEM and demonstrated that the incident electron dose can be traded off with a tolerable increase in imaging errors. The work in this thesis improves the dose reduction possible with quantum imaging and adaptive illumination schemes and represents a major step towards their implementation in different types of electron microscopes.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
 
Cataloged from student-submitted PDF of thesis.
 
Includes bibliographical references (pages 257-267).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/129240
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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