Towards a low-loss aluminum nitride on insulator (AlNOI) platform for integrated photonics
Bibliographic record
Abstract
Aluminum nitride (AlN) holds significant potential for near- and mid-infrared integrated photonics, particularly in high-speed telecommunications and sensors. However, relatively high material absorption losses in sputtered AlN films limit performance. This study investigates continuous and cyclic annealing protocols designed to reduce material losses in the near-infrared regime for an AlN-on-insulator (AlNOI) integrated photonics platform fabricated by pulsed-DC magnetron sputtering on 8-inch Si wafers. The effects of annealing on AlN microstructure and residual stress were characterized using X-ray diffraction, micro-Raman and infrared spectroscopies. Best results were obtained with a 5-cycle 900 °C - 1350 °C thermal treatment, resulting in a rocking curve of 0.85°, a c-axis tilt angle of 0.7° relative to the surface normal, and an 85% relaxation of residual tensile stress compared to unannealed samples, demonstrating a highly textured structure. Material absorption losses were characterized using strip waveguides fabricated by e-beam lithography. By conditioning the AlNOI wafers with the 5-cycle profile prior to waveguide fabrication, material absorption losses were reduced by 63%, from 1.44 dB/cm to 0.54 dB/cm (scattering from vertical sidewall roughness in waveguides fabricated by e-beam lithography contributed additional losses of 0.13 dB/cm). Finally, with regard to electro-optic applications, the effect of the annealing protocols on the electrical properties of the films was characterized using vertical metal-insulator-semiconductor (MIS) and planar metal-semiconductor-metal (MSM) structures. By conditioning the AlNOI wafers with cyclic annealing prior to device fabrication, film resistivity and breakdown field increased exponentially with the number of cycles to 2 × 10 14 Ω·cm and 2.95 MV/cm after five cycles, respectively, while the leakage current decreased by 2 to 3 orders of magnitude depending on the applied voltage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".