Hexacoordinated Gallium(III) Triazenide Precursor for Epitaxial Gallium Nitride by Atomic Layer Deposition
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Gallium nitride (GaN) is the main component of modern-day high electron mobility transistors due to its favorable electronic properties. As electronic devices become smaller with more complex surface architecture, the ability to deposit high-quality GaN films at low temperatures is required. Herein, we report a new highly volatile Ga(III) triazenide precursor and demonstrate its ability to deposit high-quality epitaxial GaN by atomic layer deposition (ALD). This new Ga(III) triazenide, the first hexacoordinated Ga–N bonded precursor used in a vapor deposition process, was easily synthesized and purified by either sublimation or recrystallisation. Thermogravimetric analysis showed single-step volatilization with an onset temperature of 155 °C and negligible residual mass. Three temperature intervals with self-limiting growth were observed when depositing GaN films. The GaN films grown in the second growth interval at 350 °C were epitaxial on 4H–SiC without an AlN seed layer and found to have a near stoichiometric Ga/N ratio with very low levels of impurities. In addition, electron microstructure analysis showed a smooth film surface and a sharp interface between the substrate and film. The band gap of these films was 3.41 eV with the Fermi level at 1.90 eV, showing that the GaN films were unintentionally n-type-doped. This new triazenide precursor enables ALD of GaN for semiconductor applications and provides a new Ga(III) precursor for future deposition processes.
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".