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

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

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsFibics (Canada)
Fundersnot available
KeywordsFocused ion beamMaterials scienceNanoindentationMicrometerComposite materialPerpendicularSample preparationIndentationDeformation (meteorology)Displacement (psychology)GeometryMechanical engineeringIon

Abstract

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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.

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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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.301
Teacher spread0.293 · 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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