The isolation feature geometry dependence of reverse gate-leakage current of AlGaN/GaN HFETs
Bibliographic record
Abstract
Abstract Reverse gate-leakage current of AlGaN/GaN heterojunction field-effect transistors (HFETs) realized on array of submicron sized fins and conventional mesa isolation feature geometries is investigated at room temperature and zero drain-source bias. For each of the abovementioned device categories, the significance of leakage from the top surface gate as well as gated etched GaN surfaces, especially sidewalls, is studied for a wide range of gate-source voltages (VGS) (i.e. below and above the threshold voltage). It is proven that in the explored fin-type HFETs, for all values of VGS leakage through the gated GaN surfaces, especially the sidewalls, is more significant than the leakage from the top surface gate. This is while in the mesa category, the sidewall leakage is of importance only at less negative values of VGS, and leakage from the top surface gate substantially takes over at more negative VGS values. The discrepancy in the dominance of the aforementioned leakage paths at more negative VGS values among the explored fin and mesa-type HFETs is demonstrated to be due to the stronger electric field across the barrier in the gated region of the mesa-type HFET for this range of VGS. While in the explored fin-type HFETs Ion/Ioff ratio is as high as 2 × 107, the total amount of reverse gate-leakage at all values of VGS is substantially larger compared to the mesa category sharing an equal value of the overall gate width, which substantiates the significance of leakage through etched GaN surfaces in devices composed of larger number of sidewalls, incorporating larger area of gate-overlapping etched GaN surface.
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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".