Surface Chemistry and Development of Group 11 and 13 Thin Film Vapour Deposition Precursors
Bibliographic record
Abstract
Techniques for depositing thin films of metals or ceramics, such as atomic layer deposition (ALD) and chemical vapour deposition (CVD), are well established and used in a wide variety of industries and applications, such as for dielectric layers, passivation coatings, surface functionalization, conductive layers, catalysis, anti-reflection coatings, optical property modification, etc. These techniques make use of a series of vapourous precursor/solid substrate interactions, typically at elevated temperatures and low pressures, to ultimately deposit a thin, conformal, uniform film of desired material. The nature of this vapour/solid surface chemistry is paramount to determining the what material is deposited as well as its properties. Determining the specific chemistry occurring at the vapour/solid interface is not a trivial task and typically requires expensive and potentially complicated characterization techniques. Generally, ALD and CVD processes of novel materials typically suffer from purity and uniformity issues that prevents them from being widely adopted by industry. By experimentally determining the surface chemistry of these processes it is possible to logically assess and modify existing processes to address these issues. This work examined the surface chemistry of several group 11 and group 13 vapour deposition precursors using a variety of characterization techniques, primarily solid-state nuclear magnetic resonance spectroscopy (SS-NMR). In group 11, several novel Cu ALD precursors were studied, including a copper(I)-tert-butyl-iminopyrrolidinate and several copper(I)-hexamethyldisilazide-N -heterocyclic carbene complexes, as well as a novel Au ALD precursor; a Me 3 AuPMe 3 complex. By using ex situ characterization techniques such as SS-NMR ( 13 C and 29 Si), high-resolution NMR (HR-NMR) ( 1 H and 13
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".