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Record W2144314905 · doi:10.1109/tmag.2005.855289

Interaction effect on switching behaviors of paired "Pac-Man" array

2005· article· en· W2144314905 on OpenAlexaff
H. Han, Yang‐Ki Hong, M.H. Park, B. C. Choi, S. H. Gee, J. Jabal, Gavin S. Abo, Andrew Lyle, Byron Wong, G.W. Donohoe

Bibliographic record

VenueIEEE Transactions on Magnetics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCoercivityPermalloyFace (sociological concept)Materials scienceVortexHysteresisMiniaturizationCondensed matter physicsComputer sciencePhysicsMagnetic fieldNanotechnologyMagnetizationMechanics

Abstract

fetched live from OpenAlex

Interactions between neighboring cells become increasingly important due to the miniaturization of magnetoelectronic devices. This paper studies the effect of magnetic interaction on the switching behaviors in two different configurations of paired Pac-man shape Permalloy elements: back-to-back and face-to-face configurations. From as-patterned state MFM images, it is observed that the face-to-face configuration is prone to form either two single domains with an antiferromagnetic configuration, one single domain with one vortex or a double vortex configuration. MOKE hysteresis loops show that the coercivity for the face-to-face configuration is smaller than the back-to-back configuration. These experimental results indicate that the back-to-back configuration has weaker interaction between the two Pac-man elements than the face-to-face configuration. We further varied the aspect ratio of Pac-man elements in the pair arrays to tune the magnetic interaction. It was found that the coercivity of pair array increased with the higher cell aspect ratio. Micromagnetic simulation was also performed to simulate the switching process for the two different configurations. Overall, the back-to-back configuration is recommended for applications that demands less inter-cell interactions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.987

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 communication0.0000.001
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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