A Parametric Technique for Trap Characterization in AlGaN/GaN HEMTs
Bibliographic record
Abstract
A new parametric and cost-effective technique is developed to decouple the mechanisms behind current degradation in AlGaN/GaN high-electron mobility transistors (HEMTs) under a normal device operation: self-heating and charge trapping. Our unique approach investigates charge trapping using both source (IS) and drain (ID) transient currents for the first time. Two types of charge-trapping mechanisms are identified: 1) bulk charge trapping occurring on a timescale of less than 1 ms and 2) surface charge trapping with a time constant larger than a millisecond. Through monitoring the difference between IS and ID, a bulk charge-trapping time constant is found to be independent of both drain (VDS) and gate (VGS) biases. Surface charge trapping is found to have a much greater impact on slow degradation than bulk trapping and self-heating. At a short timescale (1 ms), the dynamic ON-resistance degradation is predominantly limited by surface charge trapping.
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 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".