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Data Processing for Atomic Resolution Electron Energy Loss Spectroscopy

Published online by Cambridge University Press:  15 June 2012

Paul Cueva
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
Robert Hovden*
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
Julia A. Mundy
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
Huolin L. Xin
Department of Physics, Cornell University, Ithaca, NY 14853, USA
David A. Muller
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA Kavli Institute at Cornell for Nanoscale Science, Ithaca, NY 14853, USA
Corresponding author. E-mail:
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The high beam current and subangstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopy (EELS) mapping with atomic resolution. These spectral maps are often dose limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map, one can improve background estimation and increase chemical sensitivity. We consider two such approaches—linear combination of power laws and local background averaging—that reduce background error and improve signal extraction. Principal component analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data are PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis.

Special Section: Aberration-Corrected Electron Microscopy
Copyright © Microscopy Society of America 2012

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