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Record W3017268704 · doi:10.1063/1.5143229

[(FeCoB/Ru/FeCoB)/ZnO]n superlattice multilayer: A real optical mode ferromagnetic resonance thick-film

2020· article· en· W3017268704 on OpenAlexaff
Honglei Du, Qian Xue, Shandong Li, Guo‐Xing Miao, Xian‐Ming Chu, Youyong Dai, Meijie Yu, Guoxia Zhao, Shishen Yan

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China-Yunnan Joint FundNational Natural Science Foundation of China
KeywordsSuperlatticeMaterials scienceFerromagnetic resonanceAntiferromagnetismFerromagnetismStackingResonance (particle physics)Condensed matter physicsOptoelectronicsNuclear magnetic resonanceMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

A pure optical mode (OM) resonance with ultrahigh resonance frequency (frO) and permeability μ at zero bias field was observed in 50-nm FeCoB/Ru/FeCoB sandwich trilayers due to the presence of strong antiferromagnetic interlayer exchange coupling. However, the necessary thickness for creating practical microwave devices cannot be readily achieved by increasing the FeCoB layer thickness or stacking FeCoB/Ru units due to dramatically deteriorated high-frequency performance. In this study, several 50-nm FeCoB/Ru/FeCoB OM units were stacked to form a superlattice structure, with 10-nm ZnO insulator spacers separating the units. It is interesting that the superlattice multilayers not only retain excellent high-frequency OM resonance performance comparable to the single OM unit but also increase the effective magnetic film thickness by a factor of 5. This can be attributed to the positive superposition of the almost identical OM units as well as the effective decoupling between them by thick ZnO spacers. This study provides a promising way to fabricate thicker films while still maintaining excellent OM resonance performance.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.222
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

Citations6
Published2020
Admission routes1
Has abstractyes

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Same venueApplied Physics LettersSame topicMagnetic properties of thin filmsFrench-language works237,207