Integrated InAs/InP quantum-dot coherence comb lasers
Bibliographic record
Abstract
Current communication networks needs to keep up with the exponential growth of today’s internet traffic, and telecommunications industry is looking for radically new integrated photonics components for new generation optical networks. We at National Research Council (NRC) Canada have successfully developed nanostructure InAs/InP quantum dot (QD) coherence comb lasers (CCLs) around 1.55 m. Unlike uniform semiconductor layers in most telecommunication lasers, in these QD CCLs light is emitted and amplified by millions of semiconductor QDs less than 60 nm in diameter. Each QD acts like an isolated light source acting independently of its neighbours, and each QD emits light at its own unique wavelength. The end result is a QD CCL is more stable and has ultra-low timing jitter. But most importantly, a single QD CCL can simultaneously produce 50 or more separate laser beams at distinct wavelengths over the telecommunications C-band. Utilizing those unique properties we have put considerable effort well to design, grow and fabricate InAs/InP QD gain materials. After our integrated packaging and using electrical feedback-loop control systems, we have successfully demonstrated ultra-low intensity and phase noise, frequency-stabilized integrated QD CCLs with the repetition rates from 10 GHz to 100 GHz and the total output power up to 60 mW at room temperature. We have investigated their relative intensity noises, phase noises, RF beating signals and other performance of both filtered individual channel and the whole CCLs. Those highly phase-coherence comb lasers are the promising candidates for flexible bandwidth terabit coherent optical networks and signal processing applications.
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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".