Erbium doped silicon rich silicon oxide luminescent thin films deposited by ECR-PECVD
Bibliographic record
Abstract
The development of monolithically integrated optoelectronics in silicon has been hindered to date primarily because of silicon's inefficient optical emission properties. Recently, however, nanostructured systems exploiting quantum confinement effects have shown the potential to circumvent this problem. In this study, silicon-rich silicon oxide (SiOx, x<2) thin films doped with erbium have been deposited on silicon substrates by electron cyclotron resonance plasma enhanced chemical vapour deposition (ECR-PECVD). The formation of silicon nanoclusters along with optically active erbium ion complexes during high temperature annealing results in strong erbium photoluminescence near a wavelength of 1.54 μm. A portion of the deposition parameter space for the ECR-PECVD system has been mapped in an attempt to optimize the films for this luminescence. The resulting films ranged in composition from 0% to 22% excess silicon and 0.45% to 3.7% erbium, as determined by Rutherford Backscattering Spectroscopy. The effects of annealing were investigated between 600 oC and 1000 oC under flowing nitrogen gas. The 1.54μm emission was found to be enhanced by the presence of excess silicon, reaching a maximum at ~5-8 atomic % excess and an 800 oC anneal. This result strongly suggests the sensitization of infrared, erbium luminescence by silicon nanoclusters. The films exhibited an additional blueviolet light emission which has also been attributed to the erbium dopant. The visible and infrared luminescence signals were found to occur in inverse proportion to each other with the visible signal decreasing as the amount of silicon excess increases.
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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".