Trends in Copper Precursor Development for CVD and ALD Applications
Bibliographic record
Abstract
The continued dominance of copper in microelectronic manufacturing is due in part to the techniques that have kept pace with the relentless trend towards smaller feature sizes. Pure and defect-free copper features can be created at these and smaller scales using gas phase deposition methods such as chemical vapor deposition (CVD) and atomic layer deposition (ALD). Here we review the deposition processes and in particular surface chemistry for depositing copper metal by CVD and ALD. A summary of known processes is given, and new trends in copper film deposition research are discussed. As well, process parameters and properties of copper films deposited from precursors using key ligand systems such as aminoalkoxides, amidinates, guanidinates, betadiketonates and betaketoiminates are presented. Surface chemistry is examined from the point of view of the similarities of CVD and ALD, considering precursors that can be used in both types of processes. This serves to highlight trends in decomposition mechanisms and illuminates some interesting similarities in process temperature and other parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".