Planar lightwave circuits: it's all in the cladding
Bibliographic record
Abstract
The top cladding layer in planar lightwave circuits (PLC) is more than an optical buffer. By variously doping, adjusting the thickness of, etching patterns in and annealing the cladding layers in waveguide devices, a wide range of sensors and photonic devices can be realized. The material properties of the cladding determine, for instance, the modal birefringence of the waveguides; knowledge and control of these properties can be harnessed to produce polarization-independent components. The fabrication of thermo-optically controlled switches and interferometers for tunable filtering and optical signal processing is possible through the creation of micro heaters on top of the cladding. The optimization of such components can benefit from engineering of the cladding, ranging from better planarization and thickness control, to selective etching to better confine the heat distribution and provide stress relief. In addition, the thermal properties of a given device can be radically enhanced by using a polymer layer as top cladding, which yields an order of magnitude increase in the temperature sensitivity, an invaluable enhancement that can be harnessed for phase-tunable waveguides or sensor structures. Long period gratings (LPGs) can be etched in the lower cladding to provide filtering, signal processing, or sensor functions. In a borophosphosilicate cladding, typically used in silica-on-silicon PLCs, control of the reflow properties through composition can be exploited to manufacture fillable microchannels that are monolithically integrated with solid-core devices, enabling a unique platform for sensing, signal processing, or nonlinear optics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".