Analytical Modeling of Current Collapse in AlGaN/GaN HFETs According to the Virtual Gate Concept
Bibliographic record
Abstract
GaN-based heterostructure field effect transistors (HFETs) have gained considerable attention in high-power microwave applications. So far, unsurpassed current levels and high output power at microwave frequencies have been achieved. However, the dominant factors limiting the reliability of these devices under high-power operation are still unsettled. Drain current collapse is one of the major encumbrances in the development of reliable high-power devices in this technology. In this paper, an accurate and versatile analytical model based on the concept of virtual gate formation due to the existence of acceptor-type surface states is developed to model the current-collapse phenomenon. The presented model is considerably simple, and at the same time, it is more precise than the other analytical models previously proposed in literature. The implementation of this analytical model demonstrates superb agreement with the experimental observations of permanent/semipermanent current collapse in AlGaN/GaN HFETs. To demonstrate the versatility, results of this model are also compared with an existing recently developed analytical model of comparable degree of complexity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".