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Record W4392223941 · doi:10.48550/arxiv.2402.15910

Energy landscape of noncollinear exchange coupled magnetic multilayers

2024· preprint· en· W4392223941 on OpenAlexfundno aff
George Lertzman-Lepofsky, Afan Terko, Sabri Koraltan, Dieter Suess, Erol Girt, Claas Abert

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaAustrian Science Fund
KeywordsEnergy exchangeEnergy landscapeEnergy (signal processing)Condensed matter physicsMaterials sciencePhysicsAtmospheric sciencesQuantum mechanics

Abstract

fetched live from OpenAlex

We conduct an exploration of the energy landscape of two coupled ferromagnetic layers with perpendicular-to-plane uniaxial anisotropy using finite-element micromagnetic simulations. These multilayers can be used to produce noncollinearity in spin-transfer torque magnetic random-access memory cells, which has been shown to increase the performance of this class of computer memory. We show that there exists a range of values of the interlayer exchange coupling constants for which the magnetic state of these multilayers can relax into two energy minima. The size of this region is determined by the difference in the magnitude of the layer anisotropies and is minimized when this difference is large. In this case, there is a wide range of experimentally achievable coupling constants that can produce desirable and stable noncollinear alignment. We investigate the energy barriers separating the local and global minima using string method simulations, showing that the stabilities of the minima increase with increasing difference in the anisotropy of the ferromagnetic layers. We provide an analytical solution to the location of the minima in the energy landscape of coupled macrospins, which has good agreement with our micromagnetic results for a case involving ferromagnetic layers with the same thickness and anisotropy, no demagnetization field, and large exchange stiffness. These results are important to understand how best to employ noncollinear coupling in the next generation of thin film magnetic devices.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.942

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 head0.027
GPT teacher head0.165
Teacher spread0.138 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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