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Enregistrement W4252106318 · doi:10.1002/9783527808465.emc2016.6495

Automatic <scp>FIB‐SEM</scp> Preparation of Straight Pillars for In‐Situ Nanoindentation

2016· other· en· W4252106318 sur OpenAlexaff
Tobias Volkenandt, Alexandre Laquerre, Michał Postolski, F. Pérez‐Willard

Notice bibliographique

RevueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Langueen
DomainePhysics and Astronomy
ThématiqueForce Microscopy Techniques and Applications
Établissements canadiensFibics (Canada)
Organismes subventionnairesnon disponible
Mots-clésFocused ion beamMaterials scienceNanoindentationMicrometerComposite materialPerpendicularSample preparationIndentationDeformation (meteorology)Displacement (psychology)GeometryMechanical engineeringIon

Résumé

récupéré en direct d'OpenAlex

In‐situ indentation tests in FIB‐SEMs are a powerful tool to characterize the mechanical deformation properties of matter at the micron scale [1,2]. FIB milling is used to produce micrometer sized – usually cylindrical – pillars from the bulk, while SEM imaging allows to determine the geometry of the pillars prior, during and after the load‐displacement data acquisition. In this work, different automatic workflows were tested for the preparation of high aspect‐ratio pillars with well‐defined geometries, in particular with perfectly perpendicular side walls. A state‐of‐the‐art FIB‐SEM instrument was used to fabricate the pillars. They were machined by milling a series of concentric rings with decreasing FIB currents into the sample. Hereby, the sample was at 54° tilt to ensure normal incidence of the FIB. The last and smallest ring was milled with a 3 nA probe, which yielded a slightly material dependent pillar wall angle of around 2° to the sample normal. After this pre‐preparation step, the geometry of the pillars was refined further to achieve perfectly perpendicular pillar side walls using lathe milling [3]. The ideal cylindrical geometry is highly desirable, because it is easier to model for a reliable analysis of the load‐displacement measurement. Two different lathe milling techniques were implemented in this work and compared. They both involve a number of FIB milling steps each performed at different sample rotations to shape the pillar wall along its whole circumference. After each sample rotation the pillar needs to be repositioned accurately by means of SEM and FIB image recognition of fiducial marks on the sample. The first approach, #1, is similar to the one described in [3]. The walls of the pillar are shaped from the side by FIB milling at zero degree stage tilt as shown in Figure 1(a). For sample repositioning a single fiducial is used which is placed – for symmetry reasons – exactly in the center of the pillar (see Figs. 1(b) and (c)). Approach #1 was automated using the application programming interface (API) of the FIB‐SEM instrument. Including lathe milling the total preparation time per typical pillar adds up to about an hour. Because of the space needed for the fiducial mark only pillars with diameters, d&gt;5 µm, can be fabricated automatically in this way. The need to fabricate smaller pillars with d&lt;5 µm motivated an alternative and new lathe milling workflow, #2 (see Figure 2). Here, the walls of the pillar are shaped from the pillar top (sample at 54° tilt), as it was done in the pre‐preparation step, too. By slightly under‐tilting the sample a few degrees an edge of the pillar was exposed to the FIB for machining (Fig 2(a)). The sample was then rotated and repositioned for the next milling step. This process was iterated to cover the full circumference of the pillar. In order to reduce the number of iterations the milling was done following the green boomerang type of shape depicted in Figure 2(b). Only eight iterations – as compared to at least 18 with approach #1 – were needed to obtain an almost perfectly circular pillar cross section (see Fig. 2(c)). In summary, the new lathe milling process can be used to machine very small pillars. It can be combined easily with the pillar pre‐preparation step for a fully automatic pillar preparation. Further, because it gets along with less iterations, it is faster than previous approaches.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,076
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,008
Tête enseignante GPT0,301
Écart entre enseignants0,293 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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