A Monolithic Microring Modulator-Based Transmitter With a Multiobjective Thermal Controller
Bibliographic record
Abstract
This article presents a multiobjective thermal controller that stabilizes the resonance wavelength of silicon photonic microring modulators (MRMs) under varying temperature conditions and fluctuations in laser power. The controller operates in the background while live data is flowing, adjusting the MRM resonance wavelength to achieve optimal application-specific performance metrics, including any one of extinction ratio (ER), optical modulation amplitude (OMA), or level separation mismatch ratio (RLM). This universal bias-assisted photocurrent-based controller is capable of selectively tuning for any of these transmitter metrics without the need for broadband circuits. Notably, this is the first controller proposed to tune the MRM for optimizing RLM, which is particularly important as MRMs are now increasingly adopted for 4-PAM modulation. The controller functionality is verified on an MRM monolithically integrated in a silicon photonic 45-nm CMOS SOI process with a high-swing$4.7~{V}_{\text {pp}}$digital-to-analog converter (DAC)-based 5.5-bit resolution driver, dissipating$1.7~\text {pJ/b}$at$40~\text {Gb/s}$. With the controller optimizing for different objectives, an ER of 10.3 dB, OMA of$540~\mu \text {W}$(normallized OMA of −3.2 dB), transmitter dispersion eye closure quaternary (TDECQ) of 0.67 dB, and RLM of 0.96 are achieved without employing a nonlinear feed-forward equalizer (FFE) or predistortion.
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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".