Growth kinetics and electronic properties of unintentionally doped semi-insulating GaN on SiC and high-resistivity GaN on sapphire grown by ammonia molecular-beam epitaxy
Bibliographic record
Abstract
Growth of unintentionally doped (UID) semi-insulating GaN on SiC and highly resistive GaN on sapphire using the ammonia molecular-beam epitaxy technique is reported. The semi-insulating UID GaN on SiC shows room temperature (RT) resistivity of 1011 Ω cm and well defined activation energy of 1.0 eV. The balance of compensation of unintentional donors and acceptors is such that the Fermi level is lowered to midgap, and controlled by a 1.0 eV deep level defect, which is thought to be related to the nitrogen antisite NGa, similar to the “EL2” center (arsenic antisite) in unintentionally doped semi-insulating GaAs. The highly resistive GaN on sapphire shows RT resistivity in range of 106–109 Ω cm and activation energy varying from 0.25 to 0.9 eV. In this case, the compensation of shallow donors is incomplete, and the Fermi level is controlled by levels shallower than the 1.0 eV deep centers. The growth mechanisms for the resistive UID GaN materials were investigated by experimental studies of the surface kinetics during growth. The required growth regime involves a moderate growth temperature range of 740–780 °C, and a high ammonia flux (beam equivalent pressure of 1×10−4 Torr), which ensures supersaturated coverage of surface adsorption sites with NHx radicals. Such highly nitrogen rich growth conditions lead to two-dimensional layer by layer growth and reduced oxygen incorporation.
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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 | 0.000 |
| 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".