Transition from thermal diffusion to heat accumulation in high repetition rate femtosecond laser writing of buried optical waveguides
Bibliographic record
Abstract
A variable (0.2 to 5 MHz) repetition rate femtosecond laser was applied to delineate the role of thermal diffusion and heat accumulation effects in forming low-loss optical waveguides in borosilicate glass across a broad range of laser exposure conditions. For the first time, a smooth transition from diffusion-only transport at 200 kHz repetition rate to strong heat accumulation effects at 0.5 to 2 MHz was observed and shown to drive significant variations in waveguide morphology, with rapidly increasing waveguide diameter that accurately followed a simple thermal diffusion model over all exposure variables tested. Amongst these strong thermal trends, a common exposure window of 200 mW average power and approximately 15-mm/s scan speed was discovered across the range of 200 kHz to 2 MHz repetition rates for minimizing insertion loss despite a 10-fold drop in laser pulse energy. Waveguide morphology and thermal modeling indicate that strong thermal diffusion effects at 200 kHz give way to a weak heat accumulation effect at approximately 1 microJ pulse energy for generating low loss waveguides, while stronger heat accumulation effects above 1-MHz repetition rate offered overall superior guiding. A comprehensive characterization of waveguide properties is presented for laser writing in the thermal diffusion and heat accumulation regimes. The waveguides are shown to be thermally stable up to 800 degrees C and can be written in a convenient 520 microm depth range with low spherical aberration.
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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) | 0.000 | 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".