Silicon waveguide integrated with a tellurium oxide whispering gallery resonator on chip (Conference Presentation)
Bibliographic record
Abstract
Tellurite glasses have promising material properties in applications for linear and nonlinear integrated optical devices. Tellurite glasses have high rare earth solubilities for applications in rare earth doped lasers as well as high nonlinear refractive indices, Raman gain coefficients and acousto-optic figures of merit. However, it is difficult to take advantage of tellurite glass properties in silicon photonics, as the waveguiding materials available for use in silicon photonic devices are typically limited to silicon, silicon dioxide, silicon nitride, and germanium. Here, we report on a tellurium oxide whispering gallery resonator, integrated onto a silicon photonic chip and coupled to a silicon waveguide. The silicon waveguides are fabricated using a standard foundry process and the cladding oxide is etched in a ring shape with precise alignment to the bus waveguides at gaps from 0.2 to 1.0 μm to form the cavity. Post processing deposition of a tellurium oxide film coats the bottom of the etched oxide cavity, forming a tellurium oxide waveguiding layer, into which light can be coupled from the silicon waveguide. A resonator with a radius of 40 μm and a 1.1-μm-thick tellurium oxide coating is measured to have an internal Q-factor of greater than 1E5. These results illustrate the potential for integration of tellurite glass devices into silicon photonic microsystems. Applications of this cavity structure in optical sensing, design considerations and methods to improve performance will be discussed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".