Reverse Gate-Current of AlGaN/GaN HFETs: Evidence of Leakage at Mesa Sidewalls
Bibliographic record
Abstract
Reverse gate-current of AlGaN/GaN heterostructure field-effect transistor is studied over a wide range of lattice-temperatures from 150 to 490 K. For gate-source voltages approaching zero, volt signatures of gate-to-2-D electron gas leakage through the sidewalls of the mesa are observed. For exploring this leakage path, a set of devices built on a number of alternative isolation features of different geometries, and with different number of gate-covered sidewalls, are investigated. Among these devices, which were realized on an identical layer structure (produced following the same fabrication technology with identical gate-length and -width), as the number of isolation-feature sidewalls overlapping with the gate metal increases, an increase in the gate-current is observed. The identified sidewall leakage is not only consequential in devices built on isolation-feature geometries presenting more than two sidewalls, but it can also compete with mechanisms such as Poole-Frenkel (PF), Fowler-Nordheim, and trap-assisted tunneling (TAT), which are traditionally considered in devices built on cubic mesas. In this paper, the relevance of these other transport mechanisms is also re-evaluated. It is observed that at temperatures below 320 K, among all of the explored devices, sidewall leakage becomes more dominant than the PF and trap-assisted tunneling (TAT) processes.
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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".