Temperature-insensitive echelle wavelength demultiplexer on standard silicon-on-insulator platform
Bibliographic record
Abstract
Temperature sensitivity is an issue that severely affects many integrated silicon photonic devices. Proper circuit functionality is normally ensured by active thermal control at the expense of energy consumption. In some cases, athermal behavior can be achieved exploiting cladding materials with a negative thermo-optic coefficient to counterbalance the positive coefficients of silicon and silica. On the other hand, in echelle grating filters this method is not effective because in the slab free-propagation region the modal overlap with the cladding is small, especially for TEpolarized light. Moreover the need to add non-standard materials to the established silicon-on-insulator (SOI) fabrication process could make these solutions impractical. Here we present the design of a temperature-insensitive echelle grating demultiplexer with four channels operating in the TE polarization that does not use any materials with negative thermooptic coefficient and relies exclusively on standard processes for SOI photonics. The design exploits a temperaturesynchronized Mach-Zehnder interferometer as input to the echelle to compensate the shift of the imaged field with temperature. The device achieves a significant reduction in the temperature dependence of the overall transmission with a residual channel wavelength fluctuation smaller than 45 pm over a temperature range of 20 K, compared to a 1.6-nm shift for the same grating with a conventional waveguide input. The excess loss due to the use of the Mach-Zehnder input is no more than 0.7 dB for all four channels. Furthermore, the proposed design shows a very good tolerance to fabrication uncertainty, with minimum degradation of the performance for waveguide width variations of 10 nm.
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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 | 0.001 | 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".