<i>AxBAxB</i>… pulsed atomic layer deposition: Numerical growth model and experiments
Bibliographic record
Abstract
Atomic layer deposition (ALD) is widely used for the fabrication of advanced semiconductor devices and related nanoscale structures. During ALD, large precursor doses (&gt;1000 L per pulse) are often required to achieve surface saturation, of which only a small fraction is utilized in film growth while the rest is pumped from the system. Since the metal precursor constitutes a significant cost of ALD, strategies to enhance precursor utilization are essential for the scaling of ALD processes. In the precursor reaction step, precursor physisorption is restricted by steric hindrance (mA1) from ligands on the precursor molecules. On reaction, some of these ligands are removed as by-products resulting in chemisorbed species with reduced steric hindrance (mA1 → mA2, where mA2 &lt; mA1) and some of the initially hindered surface reaction sites becoming accessible for further precursor physisorption. To utilize these additional reaction sites, we propose a generalized AxBAxB… pulsed deposition where the total precursor dose (ΦA) is introduced as multiple x (x &gt; 1, x ∈ I) short-pulses rather than a single pulse. A numerical first-order surface reaction kinetics growth model is presented and applied to study the effect of AxBAxB… pulsed ALD on the growth per cycle (GPC). The model calculations predict higher GPC for AxBAxB… pulsing than with ABAB… deposition. In agreement with the model predictions, with AxBAxB… pulsed deposition, the GPC was found to increase by ∼46% for ZrN plasma enhanced ALD (PEALD), ∼49% for HfO2 PEALD, and ∼8% for thermal Al2O3 ALD with respect to conventional ABAB… pulsed growth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".