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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 OpenAlexaff
Vladimir Timoshevskii, Youqi Ke, Hong Guo, Daniel Gall

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.025
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

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

Citations148
Published2008
Admission routes1
Has abstractyes

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