Far-infrared properties of helium-doped silicon
Bibliographic record
Abstract
Single crystal silicon is an important material used for semiconductor devices and also a potential material in device research. Far-infrared optical properties are studied for the helium ion implanted samples with different fluences of 5.0 × 1016, 1.0 × 1017, 2.0 × 1017, and 4.0 × 1017 cm−2. The absorption coefficient and refractive index of silicon with different helium concentrations are measured in the wave number range from 4 to 85 cm−1 and temperature range from 145 to 520 K. The results show that the absorption coefficient increases with increasing fluence of helium ions, but the refractive index decreases. The correlations between absorption coefficient and refractive index and temperature of all samples are similar. The refractive index increases with temperature in the whole measurement temperature range, but the absorption coefficient has different trends in the different wavebands. The absorption coefficient decreases with the increase of temperature in the region below 12 cm−1; however, the absorption coefficient increases when the wave number is larger than 30 cm−1. The absorption coefficient curves at different wavebands are well fitted by the Drude model. A large number of helium bubbles and defects, such as voids and dislocations have been observed in the implanted area by transmission electron microscopy (TEM), which contributes to the increase of optical absorption.
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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".