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Record W2552404008 · doi:10.1063/1.4967473

Tunnel magnetoresistance in epitaxial (100)-oriented FeCo/LiF/FeCo magnetic tunnel junctions

2016· article· en· W2552404008 on OpenAlexafffund
Qian Xue, Yihang Yang, Zhiwei Gao, Fen Liu, Qiang Li, Shandong Li

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMagnetoresistanceTunnel magnetoresistanceCondensed matter physicsMaterials scienceEpitaxyFerromagnetismTunnel junctionBiasingMolecular beam epitaxyDeposition (geology)Magnetic fieldVoltageOptoelectronicsQuantum tunnellingNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

We fabricated fully epitaxial magnetic tunnel junctions with LiF tunnel barriers on Si (100) substrates with high-vacuum electron-beam deposition. By changing the thickness of the LiF barrier, tunnel magnetoresistance of up to 90% at 77 K (17% at room temperature) was observed at tLiF = 2.8 nm. The magnetoresistance ratio as a function of the LiF barrier thickness shows a similar trend with that in magnetic tunnel junctions using epitaxial MgO barriers. There is a rapid decrease of the magnetoresistance ratio with increasing bias-voltage and temperature, indicating the presence of imperfections in the LiF barriers.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2016
Admission routes2
Has abstractyes

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