Surface reaction kinetics in atomic layer deposition: An analytical model and experiments
Bibliographic record
Abstract
Atomic layer deposition (ALD) surface reactions are comprised of several elementary surface interactions (such as physisorption, desorption, and chemisorption) occurring at the substrate. Since ALD processes are often far from thermodynamic equilibrium, the surface saturation behavior is controlled by the kinetics of these involved interactions. In this article, we present a first-order kinetic model for ALD reaction, to simulate the cumulative effect of precursor exposure (tA), post-precursor purge (tP1), reactant exposure (tB), post-reactant purge (tP2), and substrate temperature (Tsub) on the resulting growth per cycle (GPC) in an ABAB… pulsed ALD process. Furthermore, to simulate the effect of inadequate reactor purges (tP1, and/or tP2) and undesired non-ALD side reactions, reaction pathways to account excess GPC are also taken into consideration. From our model calculations, we simulate GPC vs Tsub trends observed in ALD growth experiments and demonstrate that the process temperature window (ΔTALD) for a constant GPC depends upon the deposition cycle parameters tA, tP1, tB, and tP2. The modeled GPC vs Tsub trends are discussed and compared with SiNx, ZrN, and ZnO PEALD growth experiments.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".