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Record W2566831599 · doi:10.1063/1.4972030

Large influence of capping layers on tunnel magnetoresistance in magnetic tunnel junctions

2016· article· en· W2566831599 on OpenAlexaff

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsNanoacademic Technologies
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsTunnel magnetoresistanceMagnetoresistanceTungstenScatteringQuantum tunnellingBrillouin zoneGiant magnetoresistanceDensity functional theoryAb initio quantum chemistry methodsAb initio

Abstract

fetched live from OpenAlex

It has been reported in experiments that capping layers, which enhance the perpendicular magnetic anisotropy (PMA) of magnetic tunnel junctions (MTJs), induce a great impact on the tunnel magnetoresistance (TMR). To explore the essential influence caused by the capping layers, we carry out ab initio calculations on TMR in the X(001)|CoFe(001)|MgO(001)|CoFe(001)|X(001) MTJ, where X represents the capping layer material, which can be tungsten, tantalum, or hafnium. We report TMR in different MTJs and demonstrate that tungsten is an ideal candidate for a giant TMR ratio. The transmission spectrum in Brillouin zone is presented. It can be seen that in the parallel condition of MTJ, sharp transmission peaks appear in the minority-spin channel. This phenomenon is attributed to the resonant tunnel transmission effect, and we explained it by the layer-resolved density of states. In order to explore transport properties in MTJs, the density of scattering states was studied from the point of band symmetry. It has been found that CoFe|tungsten interface blocks scattering states transmission in the anti-parallel condition. This work reports TMR and transport properties in MTJs with different capping layers and proves that tungsten is a proper capping layer material, which would benefit the design and optimization of MTJs.

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 score0.539
Threshold uncertainty score0.872

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

Explore more

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