Laser Glass deposition of single-mode glass fibers for the fabrication of chip-scale photonic circuits
Bibliographic record
Abstract
The use of glass, particularly fused silica (FS), instead of polymers or semiconductors as optical waveguide material is advantageous due to broadband transmission, reduced propagation losses, and enhanced thermal and mechanical stability. In this contribution, laser glass deposition for the chip-scale fabrication of FS-based optical waveguides is investigated. We use a CO 2 laser with emission at a wavelength of 10.6 µm to weld conventional single-mode glass fibers onto a FS substrate, with the aim of maintaining the waveguide properties of the fibers. Synchronized translational and rotational axis movements allow for positioning of the waveguides in arbitrary geometries. Furthermore, a CO 2 laser-based cleaving method is introduced, which facilitates on-chip creation of waveguide end facets for optical coupling. An analysis of the cleave geometry in dependence on process parameters as well as optical coupling losses are presented. Coupling losses of 0.88 dB for a laser-cleave and propagation losses of 0.56 dB/cm for a 10 cm on-chip welded fiber were achieved. The results pave the way for on-chip integration of fiber-based systems like lasers, sensing devices or optical communication networks.
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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".