First-principles quantum transport theory of the enhanced wind force driving electromigration on Ag(111)
Bibliographic record
Abstract
Herein we examine the low-bias electromigration wind force acting on quasi-one-dimensional nanoscale features within the Landauer-B\"uttiker conduction picture. Ordinarily the electromigration force is calculated under the approximation that the nonequilibrium carrier distribution in the vicinity of a defect is the same as that in the bulk. However, this approximation is rooted in the assumption that atomic scale defects scatter all incident electrons weakly (just as electrons weakly and diffusely scatter in the bulk). We examine this assumption by calculating the mode-resolved transmission against Ag(111) step edges and atomic wires using density-functional theory within the single-particle Green's function Landauer scattering picture. Furthermore we show that those modes that scatter strongly give rise to a nonequilibrium electrochemical potential drop across a defect and an increased wind force. The results quantitatively explain previously not understood experimental observations of an enhanced electron wind force against Ag(111) step edges [O. Bondarchuk et al., Phys. Rev. Lett. 99, 206801 (2007)]. In general, the results underscore the challenging nanoscale reliability problem posed by surface electromigration in nanostructures and the need for a nonequilibrium quantum transport description of the electron wind force.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".