Scan-free KrF laser phase tuning of non-hydrogen-loaded planar lightwave circuit interferometers
Bibliographic record
Abstract
The large-scale deployment of planar lightwave circuits (PLCs) is becoming a reality, but high yield and precise phase control continue to pose challenges in mass production. We demonstrate a scan-free protocol employing a large emitting beam krypton fluoride excimer laser and no hydrogen loading to precisely tune the local refractive index of PLCs directly on the die’s measurement stage. Asymmetrical Mach–Zehnder interferometers are locally irradiated with an accumulated fluence greater than 180kJ/cm 2 without damage or increased loss, inducing reproducible wavelength shifts greater than 120 nm in the interferometer response, corresponding to refractive index shifts greater than 0.001. Accumulated fluence is the primary factor controlling the index change, while per-pulse fluence determines the speed of trimming, within limits determined by the damage onset. Aside from a 20% decrease following initial thermal treatment, the photo-induced changes are thermally stable. A small amount of photo-induced birefringence is observed, but shown to be manipulatable predictively. With potential for low-cost automated implementation, the proposed protocol offers a scalable solution to improve PLC chip deployability in areas including LiDAR and photonic computing.
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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) | 0.000 | 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 | 0.001 | 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".