Scalable Modeling of Transient Self-Heating of GaN High-Electron-Mobility Transistors Based on Experimental Measurements
Bibliographic record
Abstract
This paper details an extraction procedure to fully model the transient self-heating of transistors from a GaN HEMT technology. Frequency-resolved gate resistance thermometry (f-GRT) is used to extract the thermal impedance of HEMTs with various gate widths. A fully scalable analytical model is developed from the experimental results. In the second stage, transient thermoreflectance imaging (TTI) is used to bring deeper insights into the HEMTs' temperature distribution by individually extracting the transient self-heating of each finger. TTI results are further used to successfully validate the f-GRT results and the modeling of the thermal impedance. Overall, f-GRT is demonstrated to be a fast and robust method for characterizing the transient thermal characteristics of a GaN HEMT. For the first time to the authors' knowledge, a scalable model of the thermal impedance is extracted fully from experimental results.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".