Quantitative Fluctuation Electron Microscopy in the STEM: Methods to Identify, Avoid, and Correct for Artifacts

Abstract

Fluctuation electron microscopy can reveal the nanoscale order in amorphous materials via the statistical variance in the scattering intensity as a function of position, scattering vector, and resolution. However, several sources of experimental artifacts can seriously affect the magnitude of the variance peaks. The use of a scanning transmission electron microscope for data collection affords a convenient means to check whether artifacts are present. As nanodiffraction patterns are collected in serial, any spatial or temporal dependence of the scattering intensity across the series can easily be detected. We present examples of the major types of artifact and methods to correct the data or to avoid the problem experimentally. We also re-cast the statistical formalism used to identify sources of noise in view of the present results. The present work provides a basis on which to perform fluctuation electron microscopy with a high level of reliability and confidence in the quantitative magnitude of the data.

Medium range structural order in amorphous tantala spatially resolved with changes to atomic structure by thermal annealing

“…Additionally, we observed no noticeable dependence of the variance upon the specimen thickness, which was determined by plotting the main variance peaks of 56 FEM signals (100 diffraction patterns each), from parallel lines normal to the small thickness gradient in the dataset, for both the 300°C and 600°C data sets. These results confirm that any differences in the volumes of the samples studied were inconsequential to our analysis [39]. The 400°C data was sampled from areas similar to the 300°C and 600°C data sets and underwent identical sample preparation, indicating minimal thickness variation within each sample and from sample to sample.…”

“…Li et al [39] provided a basis upon which FEM can be performed with confidence in the quantitative magnitude of the data, which entails the identification and removal of common artefacts in the dataset. We examine these noise sources here in relation to our data.…”

“…Inspired by methods used in image analysis for feature tracking, this approach renders the data invariant to inconsistencies in the illumination conditions during data acquisition [38], and removes the need to normalise after the variance is computed. It has also been shown that reliable extraction of information using the formal FEM technique is highly dependent upon the quality and reproducibility of the experimental data, and as such, steps have been laid out to accurately identify and correct artefacts in affected datasets [39] which we review in relation to our data.…”

MS–STEM–FEM: A parallelized multi-slice fluctuation TEM simulation tool

“…It has been shown that microscope variation and sample thickness contribute significantly to STEM-FEM measurements [8]. It follows that an approach of simulating the microscope beam and experimental procedure is an appealing means of including influential non-idealities into FTEM simulations.…”

Effect of medium range order on pulsed laser crystallization of amorphous germanium thin films

“…More detailed descriptions of the FEM technique can be found elsewhere. 22,23 We carried out the FEM measurements on the Zeiss Libra microscope at the National Center for Electron Microscopy (NCEM) at the Molecular Foundry at Lawrence Berkeley National Laboratory (LBNL). The Libra operates at 200 kV in STEM mode and we formed a 2.0 6 0.1 nm (FWHM) electron probe with a convergence half-angle of 1.0 mrad using a 1.5 lm condenser aperture.…”