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Record W2133755870 · doi:10.1109/mtdt.2003.1222366

An electrical simulation model for the chalcogenide phase-change memory cell

2004· article· en· W2133755870 on OpenAlexafffund
David Salamon, B.F. Cockburn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsChalcogenideMaterials scienceAmorphous solidPhase-change memoryRecrystallization (geology)Joule heatingElectrical resistance and conductanceChalcogenide glassPhase (matter)Quenching (fluorescence)Melting pointPhase transitionOhmic contactOptoelectronicsComposite materialThermodynamicsOpticsCrystallographyChemistryPhysics

Abstract

fetched live from OpenAlex

Chalcogenide glass is being investigated by several companies as the basis for a scalable and embeddable nonvolatile phase-change memory technology. One phase is a high-resistance amorphous phase that is obtained by melting a small volume of glass using ohmic heating, and then quenching it. The second phase is a low-resistance crystalline phase that is obtained by heating the glass to just below the melting point to promote recrystallization. This paper describes two models for such a cell. The first is a very simple single-element, lumped model that exhibits correct phase transition behavior, but is unrealistic in its sensitivity to the heating current pulses. The second, multiple-element model is able to more realistically represent cell heating and cooling behavior, and appears to be the more suitable basis for an electrical simulation model.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.108
GPT teacher head0.349
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 source (direct Gemma or distilled Codex), 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

Citations10
Published2004
Admission routes2
Has abstractyes

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