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Enregistrement W4412911619 · doi:10.1093/mam/ozaf048.061

“4D SEM” aka EBSD – Lessons Learned & Opportunities Presented

2025· article· en· W4412911619 sur OpenAlexaff
T. Ben Britton, Ruth Birch

Notice bibliographique

RevueMicroscopy and Microanalysis · 2025
Typearticle
Langueen
DomaineMaterials Science
ThématiqueElectron and X-Ray Spectroscopy Techniques
Établissements canadiensUniversity of British Columbia Hospital
Organismes subventionnairesnon disponible
Mots-clésAKAElectron backscatter diffractionMaterials scienceMetallurgyComputer scienceMicrostructureLibrary science

Résumé

récupéré en direct d'OpenAlex

It is exciting, insightful and useful to collect and analyze 2D micrographs, where each measurement point contains a pixelated ‘2D’ diffraction pattern. In the scanning electron microscope (SEM), this technique could have been called “4D SEM”, however in ∼1987 Dingley and co-workers at Bristol University reported some of the first integrated systems that could perform this, calling it “electron backscatter diffraction” (EBSD) [1]. In brief, Dingley et al. recognized the value of microstructure mapping by combining a scanning electron microscope together with a phosphor scintillator, low light TV camera, and automated on-line/near-line analysis routines (see Figure 1) to generate rich and easy to interpret microstructural maps which are collected in near-real-time. These maps can be used to describe properties such as crystallographic texture, phase fraction, elastic strain variations and even evidence of plastic strain (a more complete history can be found in [2] – including EBSD before TV cameras were being used). Over the past ∼38 years, EBSD has advanced significantly through contributions from an international user community and commercial vendors, becoming a cornerstone technique in many labs worldwide. Notable developments include the introduction of transmission Kikuchi diffraction (TKD) by Keller and Geiss in 2011 [3], enabling analysis of 10 nm domains using electron-transparent thin films in the SEM. Furthermore, the flexibility of SEM chambers allows new EBSD/TKD applications such as in situ experiments (e.g., heating, Fig. 2A), STEM-in-SEM with FIB lift-outs (Fig. 2B), large area analysis (Fig. 2C), tomography via serial sectioning with FIB-SEM (Fig 3D), and multi-modal analysis through simultaneous acquisition & analysis of signals like energy-dispersive X-ray (EDS/X) spectra (Fig. 2E). The popularity of EBSD likely stems from several factors: (a) an active community of practitioners, developers, and vendors; (b) advancements in hardware and software, including automation, fast pattern collection and online analysis; and (c) versatile use cases with rich multi-modal data collection and easy-to-access analysis, where EBSD helps address challenges in fields like materials science and engineering, earth sciences, and more. Looking ahead, the combination of user-friendly commercial software and a robust open-source community will drive further adoption & new analysis approaches that will ultimately benefit more fields and industries (e.g., steel production, additive manufacturing, aeroengine materials, semiconductor characterization, minerology) and open up new research areas (e.g., clean tech, including beam-sensitive perovskite solar cells [4]). Advances in SEM architectures also allow for custom hardware solutions, integrated workflows for routine in situ experiments (e.g., heating, cooling, and mechanical testing), and low-cost EBSD systems using compact direct electron detectors [5]. As we look towards the future, we can see applications driven use of machine learning tools to amplify signal to noise and rapidly reduce large data sets that can reveal new information (e.g. ordered precipitates in a Co/Ni-based superalloy [6]). Further advances include the seamless merging of chemical and structural data to achieve spatial resolutions to resolve the distribution of precipitates with very high spatial resolution (10s of nm) across large areas in bulk samples [7] that have had limited sample preparation (as compared to TEM-based lamella prep). Finally, it is now possible to directly borrow algorithms from the EBSD community and apply these to STEM-data, e.g. through the automated analysis of Kikuchi patterns in the TEM [8], as well as the opportunity to draw upon the many years of experience in the analysis of SEM-based 2D microstructure analysis of crystal orientation, phase and chemical variation. In this presentation, we will explore how these historical approaches have provided a rich playground for new microstructural analysis and understanding of materials, to hopefully prompt us to consider how we might see the blending of EBSD and 4D-STEM developments in the future [10]. Historical and current EBSD developments: (A) the initial set up proposed by Dingley and co-workers in 1987 with their online interface (adapted from [1]); (B) the rapid acceleration of EBSD pattern acquisition speeds allowing for increased data collection (adapted from [9]); (C) in-chamber IR-camera images showing a modern EBSD-set up (Aztec), in an AMBER-X plasma focused ion beam scanning electron microscope set up for 3D analysis; (D) a modern computer interface for online experimental set up, indexing, and real time analysis; (E) an off-axis TKD experimental set up, in the same microscope as C. Example EBSD experiments from our lab: (A) high temperature mapping of Zr-microstructures; (B) high spatial resolution mapping of steel microstructures, revealing sub-μm retained austenite, in a STEM-in-SEM TKD experiment; (C) a 5x5 mm2 large area map of Mg grains; (D) a 3D volume collected by pFIB-SEM based EBSD of steel, with a (100 nm)3 voxel size; (E) phase analysis in steel, revealing a Nb/Ti-C precipitate in a ferrite matrix using simultaneous EBSD and EDS analysis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,115

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,004
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,004
Science ouverte0,0030,004
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0340,026

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,043
Tête enseignante GPT0,347
Écart entre enseignants0,304 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentnon

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