<i>In Situ</i> Activation of an Indium(III) Triazenide Precursor for Epitaxial Growth of Indium Nitride by Atomic Layer Deposition
Bibliographic record
Abstract
Abstract Indium nitride (InN) is characterized by its high electron mobility, making it a ground-breaking material for high frequency electronics. The difficulty of depositing high-quality crystalline InN currently impedes its broad implementation in electronic devices. Herein, we report a new highly volatile In(III) triazenide precursor and demonstrate its ability to deposit high-quality epitaxial hexagonal InN by atomic layer deposition (ALD). The new In(III) precursor, the first example of a homoleptic triazenide used in a vapor deposition process, was easily synthesized and purified by sublimation. Thermogravimetric analysis showed single step volatilization with an onset temperature of 145 °C and negligible residual mass. Strikingly, two temperature intervals with self-limiting growth were observed when depositing InN films. In the high-temperature interval, the precursor underwent a gas-phase thermal decomposition inside the ALD reaction chamber to produce a more reactive In(III) compound while retaining self-limiting growth behavior. Density functional theory calculations revealed a unique two-step decomposition process, which liberates three molecules of each propene and N2 to give a smaller tricoordinated In(III) species. Stoichiometric InN films with very low levels of impurities were grown epitaxially on 4H-SiC. The InN films deposited at 325 °C had a sheet resistivity of 920 Ω/sq. This new triazenide precursor enables ALD of InN for semiconductor applications and provides a new family of M–N bonded precursors for future deposition processes.
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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".