Thermally induced refractive index trimming of visible-light silicon nitride waveguides using suspended heaters
Bibliographic record
Abstract
We demonstrate refractive index trimming of visible-light silicon nitride (SiN) waveguides using suspended heater structures. The thermal isolation of the suspended heaters enabled a semi-uniform temperature distribution with estimated temperatures of ∼350°C in the waveguides without reaching potentially damaging temperatures in the titanium nitride resistive heaters. The thermal isolation also enabled trimming temperatures to be reached with a moderate power dissipation of 30 to 40 mW. At a wavelength of 561 nm, modal effective index changes up to −8.3 × 10 −3 were observed following thermal trimming, and the index changes were stable over an observation period of 97 days. The devices were fabricated as part of our visible-light integrated photonics platform on 200-mm diameter silicon wafers. The suspended heaters also functioned as efficient thermo-optic phase shifters with power dissipation for a π phase shift of about 1.2 − 1.8 mW. The trimming method was applied to set the bias points of thermo-optic Mach-Zehnder interferometer switches to reduce the bias power of five devices from 0.29 − 2.32 mW to 0.1 − 0.16 mW. Thermal trimming at a wavelength of 445 nm was also demonstrated. Through material analysis before and after thermal treatment, we hypothesize that index trimming of the silica (SiO 2 ) waveguide cladding may be a potential underlying mechanism. Additionally, via extrapolations of the measured trimming data, we estimate the thermal aging behavior of the SiN waveguides in the suspended heaters at lower (125 - 250°C) operating temperatures.
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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) | 0.000 | 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 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".