Investigation of the DC Performance and Linearity of InAlN/GaN HFETs via Studying the Impact of the Scaling of L<sub>GS</sub> and L<sub>G</sub> on the Source Access Resistance
Bibliographic record
Abstract
In this manuscript, we have investigated the impact of the scaling of the gate-source length (LGS) and gate length (LG) on the output characterises and gate-transconductance (Gm) linearity of metallic-face InAlN/AlN/GaN heterostructure field effect transistors (HFETs) having fin structures only under the gate and those having them stretched from source to drain. Evidence for both device types suggests that the downscaling of LGS and LG augments the electron velocity in the source-access region, as a result of which the higher carrier density under the gated-channel improves the maximum drain-current density but not necessarily the$G_{m}$linearity of the device. It is shown that the devices having a planar and longer source access region are exhibiting relatively improved gate-transconductance linearity. This is suggested to be due to their almost constant source access resistance (Rs). In addition, the downscaling of the LG is observed to have a positive influence on device linearity. This observation could be due to the larger exposure to the drain-induced barrier lowering (DIBL) and the resulting rush of the carriers from the source access region to the gated-channel, leading to the suppression of the increasing$R_{s}$at higher drain currents.
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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".