Fundamental Enabling Issues in Nanotechnology: Stress at the Atomic Scale
Bibliographic record
Abstract
To effectively integrate nanotechnology into functional devices, fundamental aspects of material behavior at the nanometer scale must be understood. Stresses generated during thin film growth strongly influence component lifetime and performance; stress has also been proposed as a mechanism for stabilizing supported nanoscale structures. Yet the intrinsic connections between the evolving morphology of supported nanostructures and stress generation are still a matter of debate. This report presents results from a combined experiment and modeling approach to study stress evolution during thin film growth. Fully atomistic simulations are presented predicting stress generation mechanisms and magnitudes during all growth stages, from island nucleation to coalescence and film thickening. Simulations are validated by electrodeposition growth experiments, which establish the dependence of microstructure and growth stresses on process conditions and deposition geometry. Sandia is one of the few facilities with the resources to combine experiments and modeling/theory in this close a fashion. Experiments predicted an ongoing coalescence process that generates signficant tensile stress. Data from deposition experiments also support the existence of a kinetically limited compressive stress generation mechanism. Atomistic simulations explored island coalescence and deposition onto surfaces intersected by grain boundary structures to permit investigation of stress evolution during later growth stages, e.g., continual island coalescence and adatom incorporation into grain boundaries. The predictive capabilities of simulation permit direct determination of fundamental processes active in stress generation at the nanometer scale while connecting those processes, via new theory, to continuum models for much larger island and film structures. Our combined experiment and simulation results reveal the necessary materials science to tailor stress, and therefore performance, in nanostructures and, eventually, integrated nanocomponents.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".