Layer Dependence and Point Defect for Sub-5 nm 2D Hydrogenated GaN Transistors
Bibliographic record
Abstract
Silicon-based devices face intrinsic physical limitations in high-power and high-frequency applications due to their narrow bandgap and low breakdown strength. As an emerging postsilicon semiconductor, gallium nitride (GaN) offers significant advantages for next-generation high power electronics owing to its wide bandgap, high breakdown field strength, and outstanding radiation tolerance. In this work, we investigate the layer dependence and point defect of sub-5 nm hydrogenated GaN (H-GaN) transistors by ab-intio quantum transport simulation. The n-type H-GaN transistors with monolayer (ML), bilayer (BL), and trilayer (TL) channels all meet the ITRS on-state current targets. The ML devices yield the optimal performance with an I on of 2694 μA/μm, which exceeds those of the BL (2536 μA/μm) and TL (1974 μA/μm). Furthermore, atomic vacancy defects critically impact transport: for n-type ML devices, single N and Ga vacancies reduce I on from 2694 to 1242 and 5.72 μA/μm, respectively. Our work provides theoretical guidance for the miniaturization of future low-dimensional high-power GaN electronic devices.
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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".