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Record W3120607848 · doi:10.1063/5.0031425

Orientation control of optical mode ferromagnetic resonance: From uniaxial to omni-directional

2021· article· en· W3120607848 on OpenAlexafffund
Yicong Huang, Shouheng Zhang, Tao Sang, Guoxia Zhao, Zhejun Jin, W. Zong, Xia Wang, Jie Xu, Derang Cao, Shandong Li

Bibliographic record

VenueApplied Physics Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
FundersCanada First Research Excellence FundNational Natural Science Foundation of China
KeywordsFerromagnetic resonanceMaterials scienceAnisotropyMagnetic anisotropyCondensed matter physicsIsotropyFerromagnetismResonance (particle physics)Coupling (piping)MicrowaveMagnetic fieldOpticsNuclear magnetic resonancePhysicsMagnetizationComposite material

Abstract

fetched live from OpenAlex

Both high ferromagnetic resonance frequency (fr) and homogeneous angular performance are important for soft magnetic films to be used in high-frequency integrated circuit devices. However, high fr are obtainable only along the easy axis direction of the magnetic anisotropic materials. In uniaxially anisotropic FeCoB/Ru/FeCoB films, we could obtain an ultrahigh optical mode ferromagnetic resonance frequency (frO) up to 19.16 GHz along the easy axis under a self-bias field due to the enhancement from strong interlayer exchange coupling. However, the uniaxial intensity distribution of optical mode resonance seriously hinders the practical application in microwave components. In order to obtain the desired homogeneous angular performance, soft magnetic films with widely distributed magnetic anisotropy directions in the films were prepared and a nearly omni-directional frO with uniform values around 13.5 GHz was achieved. This study demonstrates that controlling the magnetic anisotropy's angular distribution is an effective way to obtain isotropic, self-bias magnetic films with ultrahigh fr.

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 categoriesnone
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 score0.078
Threshold uncertainty score0.759

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 head0.007
GPT teacher head0.216
Teacher spread0.208 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2021
Admission routes2
Has abstractyes

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