Bragg grating waveguide devices: Fabrication, optimization, and application
Bibliographic record
Abstract
During the last 10 years, developments in the field of ultrashort laser material processing have led to the demonstrations of numerous three dimensional photonic devices directly written inside transparent materials. However, a roadblock still exists for the commercialization of such direct writing technology, owing to a lack of effective means for inscribing high-quality Bragg gratings inside laser written waveguides that are critical for multi-functional integrated devices in optical sensing and telecom. In this thesis, for the first time, a new Bragg grating waveguide device is proposed and then fabricated directly inside transparent glass materials using the ultrashort laser direct writing technology. These Bragg grating waveguide devices are composed of arrays of partially overlapped refractive index voxels (volume pixels), which simultaneously offer low-loss light guiding and strong Bragg resonances. Two novel methods, a single-pulse writing method and a burst writing method, are introduced and each successfully demonstrated for inscribing the grating waveguide devices with respective low and high repetition rate ultrashort laser systems. Laser fabrication parameters are optimized for both methods to generate three-dimensional high-strength (>35 dB) Bragg grating waveguides with narrow-bandwidth (0.2 nm) Bragg responses tunable in the 1550-nm telecom band. Moreover, several applications of such Bragg grating waveguides are demonstrated. Thermal annealing experiments show extremely high device stability under high temperature of 500°C. Wavelength tuning and cascading of the Bragg grating waveguides are easily achieved by adjusting the laser scan velocities. A chirped Bragg grating waveguide is demonstrated that targets broadband power tap and dispersion compensation applications in telecom. At last, a distributed grating network is fabricated demonstrating unique capabilities of three-dimensional sensing of temperature, stress, and bending. These high-quality devices are the first demonstrations of high strength first order Bragg gratings inside laser written waveguides. The results of this thesis may have direct impact on fabricating novel three-dimensional devices for telecom, sensing, and biophotonics lab-on-a-chip applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".