New <scp>small‐signal</scp> extraction method applied to <scp>GaN</scp> <scp>HEMTs</scp> on different substrates
Bibliographic record
Abstract
In this article, a new extraction technique is proposed to extract the small-signal parameters of gallium nitride (GaN) high electron mobility transistors (HEMTs) on three different substrates namely, Si, SiC, and Diamond. This extraction technique used a single small-signal circuit model to efficiently describe the physical and electrical properties of GaN on different substrates. This technique takes into account any asymmetry between the gate-source and gate-drain capacitances on the asymmetrical GaN HEMT structure, charge-trapping effects, passivation layer inclusion, as well as leakage currents associated with the nucleation layer between the GaN buffer layer and the different substrates. The extracted values were then optimized using the grey wolf optimizer. The proposed technique was demonstrated through a close agreement between simulated and measured S-parameters.
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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".