Direct laser-writing of ferroelectric single-crystal waveguide architectures in glass for 3D integrated optics
Bibliographic record
Abstract
Direct three-dimensional laser writing of amorphous waveguides inside glass has been studied intensely as an attractive route for fabricating photonic integrated circuits. However, achieving essential nonlinear-optic functionality in such devices will also require the ability to create high-quality single-crystal waveguides. Femtosecond laser irradiation is capable of crystallizing glass in 3D, but producing optical-quality single-crystal structures suitable for waveguiding poses unique challenges that are unprecedented in the field of crystal growth. In this work, we use a high angular-resolution electron diffraction method to obtain the first conclusive confirmation that uniform single crystals can be grown inside glass by femtosecond laser writing under optimized conditions. We confirm waveguiding capability and present the first quantitative measurement of power transmission through a laser-written crystal-in-glass waveguide, yielding loss of 2.64 dB/cm at 1530 nm. We demonstrate uniformity of the crystal cross-section down the length of the waveguide and quantify its birefringence. Finally, as a proof-of-concept for patterning more complex device geometries, we demonstrate the use of dynamic phase modulation to grow symmetric crystal junctions with single-pass writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".