GaN Integration Technology, an Ideal Candidate for High-Temperature Applications: A Review
Bibliographic record
Abstract
In many leading industrial applications such as aerospace, military, automotive, and deep-well drilling, extreme temperature environment is the fundamental hindrance to the use of microelectronic devices. Developing an advanced technology with robust electrical and material properties dedicated for high-temperature environments represents a significant progress allowing to control and monitor the harsh environment regions. It may avoid using cooling structures while improving the reliability of the whole electronic systems. As a wide bandgap semiconductor, gallium nitride is considered as an ideal candidate for such environments, as well as in high-power and high-frequency applications. We review in this paper the main reasons that offer superiority to GaN devices over better-known technologies such as silicon (Si), silicon-on-insulator, gallium arsenide (GaAs), silicon germanium (SiGe), and silicon carbide (SiC). The theory of operation and main challenges at high temperature are discussed, notably those related to materials and contacts. In addition, the main limitations of GaN, including the technological (thermal and chemical) and intrinsic (current collapse and device self-heating) features are provided. In addition, the GaN devices recently developed for high-temperature applications are examined.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".