An integrated nonlinear optical loop mirror in silicon photonics for all-optical signal processing
Bibliographic record
Abstract
The nonlinear optical loop mirror (NOLM) has been studied for several decades and has attracted considerable attention for applications in high data rate optical communications and all-optical signal processing. The majority of NOLM research has focused on silica fiber-based implementations. While various fiber designs have been considered to increase the nonlinearity and manage dispersion, several meters to hundreds of meters of fiber are still required. On the other hand, there is increasing interest in developing photonic integrated circuits for realizing signal processing functions. In this paper, we realize the first-ever passive integrated NOLM in silicon photonics and demonstrate its application for all-optical signal processing. In particular, we show wavelength conversion of 10 Gb/s return-to-zero on-off keying (RZ-OOK) signals over a wavelength range of 30 nm with error-free operation and a power penalty of less than 2.5 dB, we achieve error-free nonreturn to zero (NRZ)-to-RZ modulation format conversion at 10 Gb/s also with a power penalty of less than 2.8 dB, and we obtain error-free all-optical time-division demultiplexing of a 40 Gb/s RZ-OOK data signal into its 10 Gb/s tributary channels with a maximum power penalty of 3.5 dB.
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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".