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Record W4205373957 · doi:10.1063/5.0078240

Magnetic-field-free spin–orbit torque-driven magnetization dynamics in CoFeB/β-W-based nanoelements

2022· article· en· W4205373957 on OpenAlexafffund
Mukesh Aryal, B. C. Choi, Th. Speliotis

Bibliographic record

VenueAIP Advances · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetizationCondensed matter physicsNanomagnetMagnetization dynamicsMaterials scienceMagnetic fieldMagnetic anisotropyMicromagneticsMagnetic momentPhysics

Abstract

fetched live from OpenAlex

A full numerical analysis, which takes into account the effects of the spin Hall effect, interfacial Dzyaloshinskii–Moriya interaction, and thermal fluctuations, is carried out in in-plane magnetized CoFeB/MgO/CoFeB/high-resistivity tungsten (β-W) nanoelements. The analysis is focused on the investigation of the underlying mechanisms of magnetic-field-free spin–orbit torque (SOT)-driven magnetization reversal process on subnanosecond time scales. It is found that the magnetization in the free magnetic layer can be electrically toggled between the parallel and antiparallel alignment with respect to the fixed magnetic layer without the assistance of an external magnetic field, in which the out-of-plane canting of the magnetic moments at the element edges plays a significant role in the nucleation and subsequent expansion of the reversed magnetization. Furthermore, the thermally activated magnetization process combined with the SOT effect is found to significantly reduce the effective energy barrier to the magnetization reversal and alter the details of the SOT-driven magnetization process in nanomagnets.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.993

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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 designObservational
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

Citations1
Published2022
Admission routes2
Has abstractyes

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