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Dark‐Field Imaging with Electron Backscatter Diffraction Patterns

2016· other· en· W4231773164 on OpenAlexaff
Raynald Gauvin, Hendrix Demers, Nicolas Brodusch

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectron backscatter diffractionScanning transmission electron microscopyOpticsDiffractionDark field microscopyElectron diffractionMaterials scienceScatteringScanning electron microscopeResolution (logic)Field emission gunImage resolutionDetectorPhysicsMicroscopyComputer science

Abstract

fetched live from OpenAlex

Dark‐field (DF) imaging can be performed by selecting a specific diffracted beam in the selected area diffraction pattern in conventional transmission electron microscope (CTEM) or in the convergent beam electron diffraction pattern in scanning transmission electron microscopy (STEM) mode [1]. The resultant micrograph provides high intensity of the objects in the probed volume that diffract in this particular direction. In contrast, dark‐field micrographs can be obtained in STEM mode by capturing the signal from a specific range of scattering angles, with the most representative example being the high‐angle annular dark‐field imaging (HAADF) [2]. This leads to a contrast mostly based on atomic number differences between the different objects analysed [3]. These techniques were developed originally for CTEM and STEM. Because DF based on scattering angles is technically easy to obtain in a scanning electron microscope (SEM) by collecting the transmitted/diffracted signals with an electron detector below the thin specimen, it has been implemented in SEMs seriously since several years. This permitted taking advantage of the high contrast and low beam damage obtained at low accelerating voltages STEM in the SEM is now routinely achieved with a spatial resolution close to 1 nm in field‐emission SEMs [4]. Despite these new possibilities, DF imaging only based on diffracted beams has not been achieved yet in a SEM. The mostly used diffraction technique in the SEM has been, since the discovery of Venables [5], electron backscatter diffraction (EBSD) which has a spatial resolution of roughly 20‐30 nm and which needs a limited bulk surface preparation compared to CTEM or STEM. EBSD is assumed to be related to the electron channeling pattern (ECP) diffraction technique by the reciprocity theorem [6], although its angular resolution is, at this time, limited by the pixel resolution of the acquisition equipment. Figure 1 is a comparison between an ECP and an EBSP acquired at 20 kV from a [001] (001) silicon wafer. In this work, pseudo‐Kikuchi patterns (EBSP) recorded via EBSD were stored and reprocessed by reporting pixels or clusters of pixels intensities from a specific location in a reference EBSP to reconstruct the final image (EBSD map). A resulting micrograph (called EBSD‐DF image) was produced with a direct link to the diffracted beams in the EBSP and hence, to the crystallography of the sample, i.e., a DF image. The origin of the contrast is then similar to that of electron channeling contrast image (ECCI) as shown in Figure 2, in which EBSD‐DF micrographs of an indented compressed iron specimen with different reflections are displayed. However, the post‐acquisition processing is an invaluable advantage over ECCI because it allows generating multiple micrographs at the same time with only one set of EBSPs recorded in a beam raster fashion. This opens new ways of extracting and using the information contained in each EBSP and the main applications, at this point, are understanding deformation behaviors and interpretation [7] of channeling contrast [8].

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.279
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes1
Has abstractyes

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