Microring Modulator Detuning for Optimal Optical 4-PAM Transmitter Performance Metrics
Bibliographic record
Abstract
Microring modulator (MRM)-based optical transmitters are of interest for communication at data rates beyond 10 Gbps offering compact size and improved energy efficiency compared to Mach-Zehnder modulators, particularly for wavelength-division multiplexing (WDM) transceivers. However, the resonance wavelength of MRMs is susceptible to process and temperature variations, potentially deviating significantly from the laser wavelength within a free spectral range (FSR). To address this challenge, it is essential to optimally adjust and detune the MRM resonance wavelength and stabilize it against temperature fluctuations. This detuning has notable impacts on critical optical transmitter metrics, including optical modulation amplitude (OMA), extinction ratio (ER), and level separation mismatch ratio (RLM). This paper investigates the influence of MRM resonance detuning on these metrics, uncovering essential trade-offs between them and providing valuable insights for the design and optimization of MRM-based optical transmitters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".