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Record W3161070551 · doi:10.1088/1361-6463/abfef7

Electronic phase separation induced non-volatile bi-polar resistive switching in spatially confined manganite microbridges

2021· article· en· W3161070551 on OpenAlexafffund
Jae‐Chun Jeon, J. Jung, K. H. Chow

Bibliographic record

VenueJournal of Physics D Applied Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsManganitePolarMaterials sciencePhase (matter)Condensed matter physicsResistive touchscreenElectric fieldElectrical resistivity and conductivityChemical physicsChemistryFerromagnetismElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Correlated manganite films exhibit functional transport properties due to the co-existence of the competing electronic phase domains which are energetically similar. Here, we investigate very large bi-polar resistive switching (RS) in spatially confined La 0.3 Pr 0.4 Ca 0.3 MnO 3 films. In this system, non-volatile bi-polar RS (up to ∼2× 10 6 %) takes place via electric field induced expansion/shrinkage of metallic phase domains, which are separated by an insulating phase domain. These effects are observed without the need of a pre-forming process. We suggest the modification of a memristor model for phase separated systems to explain the observed non-volatile bi-polar I – V characteristics. Investigations of the endurance of the RS over many switching cycles (more than 2.7 × 10 4 switching) show that it does not decay and full switching occurs with a high success rate. Furthermore, the ability to carry out switching between a number of distinct resistance levels is demonstrated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes2
Has abstractyes

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