Automatic Configuration and Wavelength Locking of Coupled Silicon Ring Resonators
Bibliographic record
Abstract
Coupled silicon ring resonator filters offer high-order spectral features such as steep roll-offs, high extinction ratios, and wide pass-bands, which are attractive to many applications in telecommunications and quantum computing systems. However, so far, their sensitivity to fabrication and temperature variations have limited the usability of such filters in practical applications. Here, by using in-resonator photoconductive heaters (IRPHs) to both sense and tune the resonance conditions of ring resonators, we demonstrate automatic configuration and wavelength locking of multiring resonator filters to an input laser's wavelength. We demonstrate the automatic configuration of a four-ring Vernier filter across a 36.7-nm wavelength range spanning the entire C-band and the wavelength locking of the same filter to counteract a practical chip temperature variation of 65 ${^\circ }$C. As IRPHs do not require additional material depositions, photodetectors, or power taps and use the same contact pads for both the sense and the tune operations, these results are achieved without compromising the cost or area of the devices. Furthermore, by localizing the feedback loops to only rely on the resonance conditions of adjacent rings, we present a tuning algorithm in which the number of iterations scales linearly with the number of coupled rings in the system. As this method does not rely on the output spectral shape of the system, it is, in general, applicable to a wide range of coupled resonator systems. Our results pave a path toward practical deployment of high-order and large-scale silicon ring resonator systems.
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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 | 0.001 |
| 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 | 0.001 |
| 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".