Analytical modelling of drain-current characteristics of AlGaN/GaN HFETs with full incorporation of steady-state velocity overshoot
Bibliographic record
Abstract
An analytical model for drain-current characteristics of AlGaN/GaN heterostructure field-effect transistors (HFETs), with incorporation of steady-state velocity overshoot and the inflection points in the electronic drift-transport characteristics, is presented. Manifestations of these transport characteristics are usually neglected in modelling the drain current of III–V HFETs. However, significance of these features in AlGaN/GaN material system, compared with other III–V technologies, requires re-evaluation of this policy. Although, for the current state of the art these features are partially masked by the parasitic features such as the drain and source contact resistance, through further improvement in the device fabrication technology they will deserve further attention. The wide peak and pronounced inflection points in the transport characteristics of AlGaN/GaN heterojunctions are modelled through considering a drift-diffusion channel rather than a drift-only transport channel. Simulation results are compared with a non-diffusion type channel that has been so far assumed in modelling the drain current of these devices. Results of the presented model indicate the role of drift-velocity overshoot in further increasing the maximum drain current of AlGaN/GaN HFETs, as the contact resistance is reduced. Comparisons with the experimental measurements are also provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".