Calibration of a Circular HV Magnetron Sputtering Source for in-Situ RE Doping of Ecr-PECVD Si-Based Thin Films
Bibliographic record
Abstract
Rare-Earth doped silicon based luminescent materials have become an attractive solution in some key areas of technological development. For instance, in the field of silicon photonics there is a drive to replace electronic on-chip components with photonic counterparts [1-2]. One of the major challenges thus far has been to provide the monolithic integration of an efficient reliable electrically driven light source. Such an element could also be used for solid state lighting, and avoid expensive III-V compounds that cannot be fully integrated into electronic drivers in a CMOS line [3]. In-situ doping of Eu3+ions in silicon oxides and oxynitrides fabricated by electron-cyclotron-resonance plasma enhanced chemical vapour deposition (ECR-PECVD) is performed. Doping is achieved by using a Circular High Vacuum Magnetron sputtering source attached to the ECR-PECVD tool. The doping concentration is varied by varying the distance of the sputtering source to the target. The hot matrix composition is varied through varying oxygen and nitrogen gas flows. The effects on the doping concentrations of the sputtering source distance to target is determined through Rutherford Backscattering Spectrometry and Variable Angle Spectroscopic Ellipsometry. Preliminary luminescence measurements are discussed. [1] Jalai, B., and Fothpour, S. “Silicon photonics,” Journal of Lightwave Technology 24, 4600-4615(2006) [2]Liu, A. Jones, R., Liao, L., Samarah-Rubio, Rubin, D., Cohen, O. Nicolaescu, R., and Paniccia, M., “A high-speed silicon optical modulator based on a metaloxide-semiconductor capacitor,” Nature 427, 615-618 (2004) [3] Ponce, F.A., Bour, D.P., “Nitride-based semiconductors for blue and green light-emitting devices”, Nature 386 (6623), 351-359 (1997)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".