Erbium-Doped Amorphous Carbon-Based Thin Films: A Photonic Material Prepared by Low-Temperature RF-PEMOCVD
Bibliographic record
Abstract
The integration of photonic materials into CMOS processing involves the use of new materials. A simple one-step metal-organic radio frequency plasma enhanced chemical vapor deposition system (RF-PEMOCVD) was deployed to grow erbium-doped amorphous carbon thin films (a-C:(Er)) on Si substrates at low temperatures (<200 °C). A partially fluorinated metal-organic compound, tris(6,6,7,7,8,8,8-heptafluoro-2,2-dimethyl-3,5- octanedionate) Erbium(+III) or abbreviated Er(fod)3, was incorporated in situ into a-C based host. Six-fold enhancement of Er room-temperature photoluminescence at 1.54 µm was demonstrated by deuteration of the a-C host. Furthermore, the effect of RF power and substrate temperature on the photoluminescence of a-C:D(Er) films was investigated and analyzed in terms of the film structure. Photoluminescence signal increases with increasing RF power, which is the result of an increase in [O]/[Er] ratio and the respective erbium-oxygen coordination number. Moreover, photoluminescence intensity decreases with increasing substrate temperature, which is attributed to an increased desorption rate or a lower sticking coefficient of the fluorinated fragments during film growth and hence [Er] decreases. In addition, it is observed that Er concentration quenching begins at ~2.2 at% and continues to increase until 5.5 at% in the studied a-C:D(Er) matrix. This technique provides the capability of doping Er in a vertically uniform profile.
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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 | 0.000 | 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) | 0.001 | 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".