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Record W3014739579 · doi:10.1063/1.5143133

Terahertz excitation of spin dynamics in ferromagnetic thin films incorporated in metallic spintronic-THz-emitter

2020· article· en· W3014739579 on OpenAlexafffund
B. C. Choi, J. Rudge, Kyle M. Jordan, T. Genet

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Victoria
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsTerahertz radiationSpintronicsMaterials scienceCondensed matter physicsMagnetization dynamicsMagnetizationTerahertz spectroscopy and technologyFerromagnetismSpin Hall effectTerahertz time-domain spectroscopyOptoelectronicsMagnetic fieldSpin polarizationPhysics

Abstract

fetched live from OpenAlex

An experimental approach to trigger ultrafast spin dynamics at frequencies of the terahertz (THz) regime is explored by directly incorporating ferromagnetic Ni80Fe20 films to a Fe/Au spintronic-THz-emitter. It is found that Ni80Fe20 magnetization is directly coupled to the terahertz magnetic fields, in which the magnetic responses of Ni80Fe20 are phase-locked with terahertz pulses. High efficiency of metallic spintronic-terahertz emitters in driving terahertz-induced magnetization dynamics is observed; the maximum precession amplitude of the out-of-plane component of the Ni80Fe20 magnetization reaches over 10% of its saturation magnetization. Analytical integrations of THz magnetic field pulses reproduce the experimental results, confirming that the underlying mechanism of the observed spin dynamics is the Zeeman coupling between the terahertz magnetic field and magnetization in the Ni80Fe20 film. Our results open up possibilities for the studies of terahertz spin dynamics by integrating highly efficient low-cost metallic spintronic-THz-emitters into magnetic thin film elements.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations12
Published2020
Admission routes2
Has abstractyes

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