Foundations of plasma enhanced chemical vapor deposition of functional coatings
Bibliographic record
Abstract
Abstract Since decades, the PECVD (‘plasma enhanced chemical vapor deposition’) processes have emerged as one of the most convenient and versatile approaches to synthesize either organic or inorganic thin films on many types of substrates, including complex shapes. As a consequence, PECVD is today utilized in many fields of application ranging from microelectronic circuit fabrication to optics/photonics, biotechnology, energy, smart textiles, and many others. Nevertheless, owing to the complexity of the process including numerous gas phase and surface reactions, the fabrication of tailor-made materials for a given application is still a major challenge in the field making it obvious that mastery of the technique can only be achieved through the fundamental understanding of the chemical and physical phenomena involved in the film formation. In this context, the aim of this foundation paper is to share with the readers our perception and understanding of the basic principles behind the formation of PECVD layers considering the co-existence of different reaction pathways that can be tailored by controlling the energy dissipated in the gas phase and/or at the growing surface. We demonstrate that the key parameters controlling the functional properties of the PECVD films are similar whether they are inorganic- or organic-like (plasma polymers) in nature, thus supporting a unified description of the PECVD process. Several concrete examples of the gas phase processes and the film behavior illustrate our vision. To complete the document, we also discuss the present and future trends in the development of the PECVD processes and provide examples of important industrial applications using this powerful and versatile technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".