Large Periphery GaN HEMTs Modeling Using Distributed Gate Resistance
Bibliographic record
Abstract
This paper reports on a new method to extract the intrinsic two-port characteristics of a high-electron-mobility-transistor considering the gate resistance distributed nature knowing the gate metal sheet resistance. The procedure is straightforward. It consists of de-embedding the extrinsic parasitic elements and access resistances, measure the gate metal sheet resistance and finally extracts the intrinsic parameters by a proposed set of direct equations. It can be integrated into most modeling approaches using electrical equivalent schematics. This original method is experimentally conducted on AlGaN/GaN MOSHEMTs on Si substrate featuring four different gate widths W (0.25, 0.5, 1, 2 mm). The interest of such an extraction procedure is shown for devices with gate width above 500 μm, which indicates its strong relevance for the modeling of large GaN transistors for power electronics. In the case of fT and fmax, the classical model has variation up-to 17.5% and 9.2% with respect to measurement while the distributed model has only 2.8% and 1.3%, respectively at W = 2 mm, which emphasized the significance of the distributed gate resistance model for large periphery GaN HEMTs devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".