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Suppressing Electron Exposure Artifacts: An Electron Scanning Paradigm with Bayesian Machine Learning

Published online by Cambridge University Press:  26 July 2016

Karl Hujsak ,
Benjamin D. Myers ,
Eric Roth ,
Yue Li  and
Vinayak P. Dravid
Karl Hujsak
Affiliation:
Department of Materials Science & Engineering, Northwestern University, 2170 Campus Dr, B553 Silverman Hall, Evanston, IL 60208, USA
Benjamin D. Myers
Affiliation:
Department of Materials Science & Engineering, Northwestern University, 2170 Campus Dr, B553 Silverman Hall, Evanston, IL 60208, USA Electron Probe and Instrumentation Center, NUANCE, Northwestern University, 2220 Campus Dr, 2036 Cook Hall, Evanston, IL 60208, USA
Eric Roth
Affiliation:
Department of Materials Science & Engineering, Northwestern University, 2170 Campus Dr, B553 Silverman Hall, Evanston, IL 60208, USA Electron Probe and Instrumentation Center, NUANCE, Northwestern University, 2220 Campus Dr, 2036 Cook Hall, Evanston, IL 60208, USA
Yue Li
Affiliation:
Applied Physics Program, Northwestern University, 2170 Campus Dr, B553 Silverman Hall, Evanston, IL 60208, USA
Vinayak P. Dravid*
Affiliation:
Department of Materials Science & Engineering, Northwestern University, 2170 Campus Dr, B553 Silverman Hall, Evanston, IL 60208, USA Electron Probe and Instrumentation Center, NUANCE, Northwestern University, 2220 Campus Dr, 2036 Cook Hall, Evanston, IL 60208, USA Applied Physics Program, Northwestern University, 2170 Campus Dr, B553 Silverman Hall, Evanston, IL 60208, USA
*
*Corresponding author. [email protected]
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Abstract

Electron microscopy of biological, polymeric, and other beam-sensitive structures is often hampered by deleterious electron beam interactions. In fact, imaging of such beam-sensitive materials is limited by the allowable radiation dosage rather that capabilities of the microscope itself, which has been compounded by the availability of high brightness electron sources. Reducing dwell times to overcome dose-related artifacts, such as radiolysis and electrostatic charging, is challenging due to the inherently low contrast in imaging of many such materials. These challenges are particularly exacerbated during dynamic time-resolved, fluidic cell imaging, or three-dimensional tomographic reconstruction—all of which undergo additional dosage. Thus, there is a pressing need for the development of techniques to produce high-quality images at ever lower electron doses. In this contribution, we demonstrate direct dose reduction and suppression of beam-induced artifacts through under-sampling pixels, by as much as 80% reduction in dosage, using a commercial scanning electron microscope with an electrostatic beam blanker and a dictionary learning in-painting algorithm. This allows for multiple sparse recoverable images to be acquired at the cost of one fully sampled image. We believe this approach may open new ways to conduct imaging, which otherwise require compromising beam current and/or exposure conditions.

Keywords

scanning electron microscopyBayesian machine learningdictionary learningdose reductionin situ imaging
Type
Technique and Instrumentation Development
Information
Microscopy and Microanalysis , Volume 22 , Issue 4 , August 2016 , pp. 778 - 788
Copyright
© Microscopy Society of America 2016 

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