Area-Selective Atomic Layer Deposition Using Si Precursors as Inhibitors
Bibliographic record
Abstract
Short-chain aminosilanes, namely, bis( N, N -dimethylamino)dimethylsilane (DMADMS) and ( N, N -dimethylamino)trimethylsilane (DMATMS), have been used as Si precursors for atomic layer deposition (ALD) of SiO 2 . In this work, the DMADMS and DMATMS Si precursors are utilized as inhibitors for area-selective ALD (AS-ALD). The inhibitors selectively adsorb on a SiO 2 surface but not on H–Si, so that SiO 2 becomes selectively deactivated toward subsequent ALD. The deactivation of the SiO 2 surface by the inhibitors was investigated using various experimental and theoretical methods, including surface potential measurements, spectroscopic ellipsometry, and X-ray photoelectron spectroscopy. Better inhibition was observed for ALD of Ru and Pt than for ALD of Al 2 O 3 and HfO 2 . Through quantum mechanical and stochastic simulations, the difference in the blocking ability for noble metal and metal oxide ALD by the aminosilane inhibitors could be attributed to the inherently partial surface coverage by the inhibitors at their saturation and the reactivity of the subsequent ALD precursors. As silane inhibitors can be easily integrated with vacuum-based processes to facilitate high volume manufacturing of upcoming electronic devices, the current study provides a potential approach for the utilization of AS-ALD in pattern fabrication inside 3D nanostructures.
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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 | 0.000 |
| 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".