<i>In-plasma</i> analysis of plasma–surface interactions
Bibliographic record
Abstract
During deposition, modification, and etching of thin films and nanomaterials in reactive plasmas, many active species can interact with the sample simultaneously. This includes reactive neutrals formed by fragmentation of the feed gas, positive ions, and electrons generated by electron-impact ionization of the feed gas and fragments, excited states (in particular, long-lived metastable species), and photons produced by spontaneous de-excitation of excited atoms and molecules. Notably, some of these species can be transiently present during the different phases of plasma processing, such as etching of thin layer deposition. To monitor plasma-surface interactions during materials processing, a new system combining beams of neutral atoms, positive ions, UV photons, and a magnetron plasma source has been developed. This system is equipped with a unique ensemble of in-plasma surface characterization tools, including (1) a Rutherford Backscattering Spectrometer (RBS), (2) an Elastic Recoil Detector (ERD), and (3) a Raman spectroscopy system. RBS and ERD analyses are carried out using a differentially pumped 1.7 MV ion beam line Tandetron accelerator generating a beam at grazing incidence. The ERD system is equipped with an absorber and is specifically used to detect H initially bonded to the surface; higher resolution of surface H is also available through nuclear reaction analysis. In parallel, an optical port facing the substrate is used to perform Raman spectroscopy analysis of the samples during plasma processing. This system enables fast monitoring of a few Raman peaks over nine points scattered on a 1.6 × 1.6 mm2 surface without interference from the inherent light emitted by the plasma. Coupled to the various plasma and beam sources, the unique set of in-plasma surface characterization tools detailed in this study can provide unique time-resolved information on the modification induced by plasma. By using the ion beam analysis capability, the atomic concentrations of various elements in the near-surface (e.g., stoichiometry and impurity content) can be monitored in real-time during plasma deposition or etching. On the other hand, the evolution of Raman peaks as a function of plasma processing time can contribute to a better understanding of the role of low-energy ions in defect generation in irradiation-sensitive materials, such as monolayer graphene.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".