Abnormal Grain Growth in Electrochemically Deposited Cu Films
Bibliographic record
Abstract
Cu interconnects are essential in advanced integrated circuits to minimize the RC delay. In manufacturing these devices, Cu is deposited electrochemically using a plating bath containing organic additives. The as-deposited nanocrystalline Cu films undergo self-annealing at room temperature to form a micronsized grain structure by abnormal grain growth. Systematic experimental studies of self-annealing kinetics on model Cu films deposited on a Au substrate suggest that the rate of grain size evolution depends primarily on the initial grain size of the asdeposited film. A model for the observed abnormal grain growth process is proposed. Assuming that desorption of the organic additives leads to mobile grain boundaries, the onset of abnormal grain growth is attributed to a sufficiently low additive concentration such that a full coverage of all grain boundaries cannot be maintained. The incubation time of abnormal growth is then a logarithmic function of the initial grain size. The probability to find a growing grain is proportional to the number of grains per unit volume. This assumption is seen to be in good agreement with the experimental observations for subsequent abnormal grain growth rates. The limitations of the proposed model and the challenges to obtain further insight into the complex microstructure mechanisms during self-annealing are delineated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".