A cost-effective technology to improve power performance of nanoribbons GaN HEMTs
Bibliographic record
Abstract
A cost-effective fabrication process is developed to improve the power performance of AlGaN/GaN High Electron Mobility Transistors (HEMTs). This process uses nitrogen ion (N+) implantation to form multiple parallel nanoribbons on AlGaN/GaN heterostructures, with a thin buffer layer (AlGaN/GaN NR-HEMTs). The stopping and range of ions in matter simulations of the N+ implantation combined with measured current-field characteristics reveal a good electrical isolation beneath the two-dimensional electron gas, resulting in substantial increase in the breakdown field of the NR-HEMTs, when compared to conventional AlGaN/GaN HEMTs. The fabricated AlGaN/GaN NR-HEMTs performed (i) an ON/OFF current ratio more than two orders of magnitude larger and (ii) a buffer leakage current more than one order of magnitude weaker than that of the conventional AlGaN/GaN HEMTs. The on-resistance, RON, and series resistance, RS, of AlGaN/GaN NR-HEMTs are both reduced by one order of magnitude when compared to those of the conventional AlGaN/GaN HEMTs. These have boosted the drive current density by up to 435%. Furthermore, we have found that the architecture of the AlGaN/GaN NR-HEMTs reduces the destructive impact of electron traps in the device. An optimized AlGaN/GaN NR-HEMT exhibited a better electrostatic integrity, a subthreshold slope of ∼210 mV/dec instead of 730 mV/dec for a conventional GaN HEMT. A higher linearity in the transconductance, gm, of NR-HEMTs is observed, twice of that of a conventional GaN HEMT. These results demonstrate the great interest of developed process technology of NR-HEMTs for high-power switching applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".