Performance Investigations of InAs/InP Quantum-Dash Semiconductor Optical Amplifiers with Different Numbers of Dash Layers
Bibliographic record
Abstract
We present here a performance comparison of quantum-dash (Qdash) semiconductor amplifiers (SOAs) with three, five, eight, and twelve InAs dash layers grown on InP substrates. Other than the number of Qdash layers, the structures were identical. The eight-layer Qdash SOA gave the highest amplified spontaneous emission power (4.3 dBm) and chip gain (26.4 dB) at 1550 nm, with a 300 mA CW bias current and at 25 °C temperature, while SOAs with fewer Qdash layers (for example, three-layer Qdash SOA), had a wider ASE bandwidth (90 nm) and larger 3 dB gain saturated output power (18.2 dBm) in a shorter wavelength range. The noise figure (NF) of the SOAs increased nearly linearly with the number of Qdash layers. The longest gain peak wavelength of 1570 nm was observed for the 12-layer Qdash SOA. The most balanced performance was obtained with a five-layer Qdash SOA, with a 25.4 dB small-signal chip gain, 15.2 dBm 3 dB output saturated power, and 5.7 dB NF at 1532 nm, 300 mA and 25 °C. These results are better than those of quantum well SOAs reported in a recent review paper. The high performance of InAs/InP Qdash SOAs with different Qdash layers shown in this paper could be important for many applications with distinct requirements under uncooled scenarios.
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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 | 0.001 |
| 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".