Multiwavelength DFB laser array utilizing combined effect of ridge width and ridge tilt
Bibliographic record
Abstract
DFB laser arrays are of interest for application in high capacity fibreoptic communication systems employing wavelength division multiplexing (WDM). The comb of wavelengths produced from such an array must be accurately controlled, both in absolute wavelengths and wavelength separation between channels. The main issue in laser arrays is the mechanism used to control the lasing wavelength. By far the most popular method for wavelength control is to vary the grating pitch. This can be done by various techniques such as direct E-beam writing, E-beam generated phase mask or by the use of stepped holographic exposure. It is also possible to control the wavelength by varying the modal index of the waveguide structure. Selective area epitaxy can be used to laterally control the active layer thickness thereby controlling the modal index. On the other hand, we have explored and demonstrated a much simpler method of mode index control by sequentially incrementing the ridge widths in the laser array as well as incrementing the tilt angle of the laser ridge. This method is novel and very attractive because it uses a uniformly grown wafer and involves fewer processing steps. In the paper, the results of this scheme are described.
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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".