Foundations of plasma enhanced chemical vapor deposition of functional coatings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it