Thermo-optic switching using liquid-core waveguides in integrated Mach-Zehnder interferometers in silica-on-silicon
Bibliographic record
Abstract
The use of materials with a high thermo-optic coefficient would lead to significant improvements in energy consumption and thermal management in optical switching and sensing devices. Most liquids rank among materials having the highest thermo-optic coefficients, along with polymers and silicon. We have developed technology to directly incorporate liquids in integrated silica-on-silicon photonic device structures. Using this technology, we demonstrate experimentally integrated Mach-Zehnder interferometers (MZIs) comprising a liquid-core waveguide in one of the interferometer arms. Because of the large differential between the thermo-optic coefficients of silica and the liquid medium, the output of this device can be modulated through the thermal control of the device chip. A high contrast ratio (more than 20 dB) in the interferometer output modulation is obtained, demonstrating that the optical loss is well balanced between the two interferometer paths. The temperature variation required to fully cycle the output state is less than 0.5 degrees Celsius. Designs for low power thermo-optic switches based on these MZI structures with integrated heating electrodes are presented. The inclusion of a second liquid-core waveguide in the "passive" interferometer arm can enable athermal and polarization insensitive devices.
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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 | 0.001 |
| Open science | 0.001 | 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".