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Record W2899956755 · doi:10.1063/1.5045697

Exchange coupling in FeCoB/Ru, Mo/FeCoB trilayer structures

2018· article· en· W2899956755 on OpenAlexaff
Tommy McKinnon, Erol Girt

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAntiferromagnetismAnnealing (glass)Ferromagnetic resonanceMaterials scienceFerromagnetismMagnetizationCondensed matter physicsCoupling (piping)Magnetic fieldMetallurgyPhysics

Abstract

fetched live from OpenAlex

In this work, bilinear (J1) and biquadratic (J2) coupling between two FeCoB layers across Ru and Mo spacer layers is studied. The investigated structures are FM1/Ru and Mo(d)/FM2, where FM1 is Fe/FeCoB, FM2 is FeCoB/NiFe, and d is the thickness of the Ru and Mo spacer layers where d is varied from 0.3 to 1.5 nm. Using a ferromagnetic resonance(FMR) model, we are able to determine J1–2J2 of all as-deposited samples and those annealed at 200 and 300 °C. FMR measurements are also used to extract Gilbert damping of the magnetic films. We also use a micromagnetic model to fit magnetization as a function of field to determine J1 and J2 independently for antiferromagnetically coupled samples. This study shows that the spacer layer thickness range, for which antiferromagnetic coupling between FeCoB layer can be achieved, is reduced with increasing annealing temperature. Antiferromagnetic coupling is not realized in samples annealed at 300 °C. The damping of magnetic layers first rapidly increases and then gradually decreases with an increase in the spacer layer thickness. The exchange coupling and spin pumping in the studied structures are responsible for this trend.

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 score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

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