Low-latency passive thermal desensitization of a silicon micro-ring resonator with self-heating
Bibliographic record
Abstract
Analog photonic information processing can be implemented with low chip area using wavelength-division multiplexed systems, which typically manipulate light using micro-ring resonators. Micro-rings are uniquely susceptible to thermal crosstalk, with negative system performance consequences if not addressed. Existing thermal sensitivity mitigation methods face drawbacks including high complexity, high latency, high digital and analog hardware requirements, and CMOS incompatibility. Here, we demonstrate a passive thermal desensitization mechanism for silicon micro-ring resonators exploiting self-heating resulting from optical absorption. We achieve a 49% reduction in thermal crosstalk sensitivity and 1 µs adaptation latency using a system with no specialized micro-ring engineering, no additional control hardware, and no additional calibration. Our theoretical model indicates the potential for significant further desensitization gains with optimized micro-ring designs. Self-heating desensitization can be combined with active thermal stabilization to achieve both responsiveness and accuracy or applied independently to thermally desensitize large photonic systems for signal processing or neural network inference.
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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".