Microscopy and Microanalysis

Table of Contents - August 2012 - Volume 18, Issue 04  

Special Section: Aberration-Corrected Electron Microscopy

Data Processing for Atomic Resolution Electron Energy Loss Spectroscopy

Paul Cuevaa1, Robert Hovdena1 c1, Julia A. Mundya1, Huolin L. Xina2 and David A. Mullera1a3

a1 School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA

a2 Department of Physics, Cornell University, Ithaca, NY 14853, USA

a3 Kavli Institute at Cornell for Nanoscale Science, Ithaca, NY 14853, USA

Abstract

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.

(Received November 08 2011)

(Accepted February 22 2012)

Key words

Correspondence:

c1 Corresponding author. E-mail: rmh244@cornell.edu