Prospects for band gap engineering by plasma ion implantation
Bibliographic record
Abstract
Abstract The suitability of plasma ion implantation (PII) for band gap engineering will be examined by calculations of the band gap's spatial variation. Plasma Ion Implantation is a method to modify the surface and subsurface properties of materials; the ions surrounding the target are forced into all plasma exposed surfaces simultaneously by virtue of high‐voltage pulses. We calculated the fluence and the ion energy distribution from the dynamic sheath model. The distribution of the ions within the target is subsequently simulated by the TRIDYN software. The concentration profiles are converted into a spatial variation of the band gap. The challenges inherent to the method are discussed by means of the examples of carbon (C) PII in silicon (Si) as well as nitrogen (N) PII in gallium arsenide (GaAs). The ion distribution within the material of the former example is suitable for the formation of the Si‐C alloy. On the other hand, the distribution of N ions in GaAs prevents the formation of the Ga‐As‐N alloy. The discussed methods could be a powerful tool for the prediction of materials properties from the plasma processing parameters, thus helping to design materials. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".