Design and development of group 13 precursors for improved vapour deposition of metal nitride thin films
Bibliographic record
Abstract
Atomic layer deposition (ALD) and chemical vapour deposition (CVD) are important techniques to deposit thin films for a variety of applications. Metal oxides and nitrides are used as passivation layers and as dielectrics, and due to the increasingly small sizes of microelectronic devices, their depositions must be precise, conformal, and of high purity. This work examines how precursor design can reduce impurities in deposited films. Several novel precursors have been designed, synthesized, and characterized, and used to deposit a variety of group 13 nitride thin films. Bidentate ligands such as guanidinates, NacNacs and azenides have been explored, as have simpler ligand systems such as amides and hydrides. The importance of precursor design is emphasized due to the fact that it enables the development of new, volatile, and thermally stable compounds and ALD processes that will deposit pure, high-quality films in a cost- and time-efficient manner.
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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) | 0.001 | 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".