InAs/InP Quantum Dash Semiconductor Coherent Comb Lasers and their Applications in Optical Networks
Bibliographic record
Abstract
We report on the design, growth, and fabrication of InAs/InP quantum dash (QD) gain materials and their use in lasers for optical network applications. A noise performance comparison between QD and quantum well (QW) Fabry-Perot (F-P) lasers has been made. By using the QD gain material we have successfully developed and assembled C-band coherent comb laser (CCL) modules with an electrical fast feedback loop control system to ensure a targeted mode frequency spacing. The frequency spacing was maintained within ±100 ppm and the operation wavelengths locked on the desired ITU grid within 0.01 nm over a period of several months. We also investigated a 25-GHz C-band QD CCL with an external cavity self-injection feedback locking (SIFL) system to reduce the optical linewidth of each individual channel to below 200 kHz in the wavelength range from 1537.55 nm to 1545.14 nm. The RF mode beating signal 3-dB bandwidth was also reduced from 9 kHz to approximately 500 Hz with this SIFL system. These QD CCLs with ultra-low relative intensity noise (RIN), ultra-narrow optical linewidth, and ultra-low timing jitter are excellent laser sources for multi-terabit optical networks. Using a 34.2 GHz QD CCL we demonstrate 10.8 Tbit/s (16QAM 48 × 28 GBaud PDM) coherent data transmission over 100 km of standard single mode fiber (SSMF) and 5.4 Tbit/s (PAM-4 48 × 28 GBaud PDM) aggregate data transmission capacity over 25 km of SSMF with error-free operation.
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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 | 0.000 | 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".