Athermal silicon subwavelength grating waveguides
Bibliographic record
Abstract
In this paper, athermal subwavelength grating (SWG) waveguides are investigated. Both numerical simulations and experimental results show that a temperature independent behaviour can be achieved by combining two materials with opposite thermo-optic coefficients within the waveguide. SU-8 polymer with a negative thermo-optic coefficient (dn/dT = -1.1x10-4 K-1) is used in our silicon SWG waveguides to compensate for silicon's positive thermo-optic coefficient of 1.9x10-4 K-1. The grating duty ratio required to achieve an athermal behavior is reported to vary as a function of the operating wavelength and the waveguide dimensions. For example, for athermal waveguides of 260 nm in height, duty ratios of 61.3% and 83.3% were calculated for TE and TM polarized light respectively for a 450 nm wide waveguide, compared to ratios of 79% and 90% for a 350 nm wide waveguide. It is also reported that with increasing width, and increasing height, a smaller grating duty ratio is necessary to achieve an athermal behaviour. A smaller fraction of silicon would hence be needed to compensate for the polymer's negative thermo-optic effect in the waveguide core. Subwavelength sidewall grating (SWSG) waveguides are also proposed here as alternatives to high duty ratio SWG waveguides that are required for guiding TM polarized light. Assuming a duty ratio of 50%, the width of the narrow segments for temperature-independent behavior is found by numerical simulations to be 125 nm and 143 nm for TE and TM polarized light, respectively.
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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".