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Preparation of high fidelity holographic vortex masks using advanced <scp>FIB</scp> milling strategies

2016· other· en· W4249585691 on OpenAlexaff
Thomas Schachinger, Andreas Steiger‐Thirsfeld, Stefan Löffler, Michael Stöger‐Pollach, Sebastian Schneider, Darius Pohl, Bernd Rellinghaus, P. Schattschneider

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceOpticsHolographyVortexRotation (mathematics)PhysicsComputer scienceMechanics

Abstract

fetched live from OpenAlex

Holographic masks (HMs) with dislocation gratings placed in the condenser system of a TEM have been proven to be a reliable and robust method to impart quantized orbital angular momentum (OAM), as well as quantized magnetic moment onto the imaging electrons [1]. These so‐called electron vortex beams (EVBs) gathered a lot of attention due to some unusual properties like topological protection [2], peculiar rotation dynamics in magnetic fields [3] and intrinsic chirality. It has been suggested to use a holographic vortex mask as a vorticity filter after the specimen, in the selected area aperture holder, in order to detect spin polarized or other chiral transitions. This would bring up the unique chance to study magnetic properties of amorphous or nanocrystalline materials because the specimen's role as a crystal beam splitter – necessary in the standard energy‐loss magnetic chiral dichroism (EMCD) geometry ‐ is obsolete in this setup. High fidelity HMs are needed for such experiments. Also, in order to achieve high vortex order separation, the grating periodicity should be very fine. To improve the signal‐to‐noise ratio of the EMCD measurements, the dimensions of the HMs need to be large. FIB milling proved to be a robust and reliable technique to produce HMs, but with ever‐increasing demands on structure size and fidelity, the ordinary milling strategy using raster‐ or serpentine scanning showed limited success. Therefore, we developed a new threefold milling strategy. The first step is to employ a so called “vector scan” technique, where “stream”‐files provide the possibility to fully control the position and dwell time of the ion beam to generate spiral milling paths for every hole in the HM structure (see Fig. 1). The next step is to reverse the milling order and ‐direction after each pass [4]. Inspired by [5], the last part consists of a position‐dependent dwell time reduction in the proximity of the hole edges to enhance the HM bar edge fidelity. Fig. 2 shows an exemplary 30 µm vortex mask with a grating periodicity of 500 nm and a thickness of roughly 700 nm. One challenge encountered with this new strategy is limited digital‐to‐analog‐converter resolution as well as memory issues for large “stream”‐files. Using a state of the art FIB, it was possible to cut 50 µm HMs and to compare the ordinary raster scanning technique to the one proposed here, see Figs. 3 and 4. These results indicate that our new threefold scanning ansatz enhances the edge quality. Howerver, issues like sample‐ and beam drift as well as the crystallinity of the mask material have to be addressed in order to further improve the fidelity of the HMs' edges.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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)0.0000.000

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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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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Citations0
Published2016
Admission routes1
Has abstractyes

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