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Record W1975080908 · doi:10.1063/1.2159415

Dynamics of laminated write elements

2006· article· en· W1975080908 on OpenAlexaff
Olle Heinonen, Alexey Nazarov, M. L. Plumer

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMagnetizationCondensed matter physicsPerpendicularVortexPhysicsState (computer science)Magnetization dynamicsMagnetic fieldGeometryMechanicsQuantum mechanicsComputer scienceMathematics

Abstract

fetched live from OpenAlex

One concern as storage technology moves to perpendicular recording is the remnant state of the writer top pole. In principal, a remnant state with a substantial magnetization density perpendicular to the recording medium can lead to the unwanted erasure of data. Also, it is desirable to have the writer reach a nonerasing remnant state as quickly as possible. One technique to reduce the magnetization in the remnant state is to laminate the pole tip with some nonmagnetic material [Y. Satoh, A. Ohtsubo, and Y. Shimada, IEEE Trans. Magn. 21, 1551 (1985); S. Wang et al., IEEE Trans. Magn. 30, 3897 (1994)]. We have performed fully micromagnetic simulations of write elements with eight, five, four, and two laminates coupled antiferromagnetically. Results are presented for recording fields, as well as for the decay of the magnetization to a remnant state. The two- and four-laminate write elements typically have a vortex induced in the pole tip, and this vortex tends to survive, even in the remnant state. This can give rise both to a slow decay as well as large remnant fields from the out-of-plane magnetization in the vortex. On the other hand, the magnetization of the eight-laminate pole tip decays by “scissoring” of the magnetization in the laminates, with a faster decay to the remnant state. However, locally large divergences of the magnetization density can give rise to “hot spots” with relatively large remnant fields.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.446

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)0.0000.000

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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 designTheoretical or conceptual
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

Citations5
Published2006
Admission routes1
Has abstractyes

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