Optical add/drop multiplexer based on subtractive dispersion planar reflective gratings
Bibliographic record
Abstract
Optical add/drop multiplexers (OADMs) have emerged as the key enabling components for building long-haul and metro-area networks. The wide-spread deployment of OADMs in the access market will depend on the availability of cost-effective integrated solutions. We have successfully fabricated a fully-integrated OADM based on planar reflective gratings. The device uses a combination of two grating elements arranged in a subtractive dispersion configuration. The first grating demultiplexes a 300-nm-wide band and drops optical channels at 1490 nm and 1550 nm, commonly used by service providers to send information to the end user. The second grating completely counter-balances the dispersion properties of the first grating and ultimately yields zero dispersion in the output waveguide. Such a configuration allows the transmission of optical signals though the OADM in an ultra-wide band spanning 1250 to 1410 nm. This ultra-wide 'through' band is a critical step allowing the use of low-cost lasers, without temperature stabilization, for sending data to a service provider. The OADM was manufactured using an industry standard silica-on-silicon process which was augmented with grating facet formation and metallization. In spite of using low refractive index contrast waveguides (0.82%), the device had a remarkably low footprint of only 0.25 square centimeters. Applications of the OADM in access market networks is discussed.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".