Electrical Doping in Sc‐III‐Nitrides: Toward Multifunctional Devices at the Single Device Level
Bibliographic record
Abstract
A homogeneous integration of various types of devices using a single material platform is an ideal route toward multifunctional devices at the single-device level for miniaturized, fast, and energy-efficient systems. However, such a single material platform is still missing. Scandium-containing III-nitrides (Sc-III-nitrides) are promising, but their electrical doping properties remain unknown. In this work, the electrical doping in Sc-III-nitrides is investigated and optoelectronic devices using Sc-III-nitrides on silicon (Si) are further demonstrated. The material format of the nanowire is used, with magnesium (Mg) serving as the impurity dopant to control the electrical doping. It is discovered that, by adjusting the Mg doping concentrations, the Sc-III-nitrides can be tuned from n-type to p-type. Device application in light-emitting is further demonstrated using the p-type Sc-III-nitrides as the hole injection layer. The performance comparison between devices using the regrown Sc-containing p-type contact layers and non-Sc-containing p-type contact layers indicates the advantage of Sc incorporation in improving the quality of the regrown p-type layer in a device structure. The electrical doping in Sc-III-nitrides demonstrated in this study represents an important step toward a homogeneous integration of different types of devices using a single material platform.
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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) | 0.000 | 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".