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Record W2007387568 · doi:10.1063/1.2937188

The influence of surface roughness on electrical conductance of thin Cu films: An <i>ab initio</i> study

2008· article· en· W2007387568 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsMcGill University
FundersSemiconductor Research Corporation
KeywordsConductanceMonolayerSurface roughnessMaterials scienceElectrical resistivity and conductivityThin filmAb initio quantum chemistry methodsIsotropyAb initioSurface finishCondensed matter physicsAtomic unitsFermi levelElectrical resistance and conductanceChemistryNanotechnologyComposite materialOpticsElectronMoleculePhysics

Abstract

fetched live from OpenAlex

First-principles calculations show that atomic-scale surface roughness dramatically affects the electrical conductivity of thin films. Atomic clusters, 1–3 atoms high, deposited on the flat Cu(001) surface of an 11 monolayer thick film lead to a 30−40% reduction of its conductance. This is attributed to the destruction of isotropic Fermi surface sheets. We provide a simple parametrized formula, correlating the size of the surface added structures to the film conductance, and also demonstrate that Ta and Al surface monolayers on rough Cu surfaces cause a conductance decrease and increase, respectively.

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.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.025
GPT teacher head0.275
Teacher spread0.250 · 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