High-resolution F 2 -laser machining of micro-optic components
Bibliographic record
Abstract
The F2-laser Nano fabrication Facility at the University of Toronto delivers high-fluence 157-nm radiation at high resolution to micro fabricate high-finesse silica-based optical components. The 7.9-eV photons drive strong material interactions near the band-edge states of fused silica and related glasses that help avoid microcrack formation, a common limitation of longer wavelength laser. The strong interactions provide for small and smooth excisions, offering depth control on a scale of tens of nanometers. A 157-nm beam homogenization system and a 25x Schwarzschild lens provided a uniform on-target fluence of 9 J/cm2 in a 0.25 mm by 0.25 mm field. Larger work are was enabled by synchronously driving the projection mask and target motion stages. The 0.4 NA lens supported the formation of high- aspect channel walls and surface-relief features as small as approximately 500 nm. Both mask projection and direct writing technique were employed. The novel aspects of the optical beam delivery system are presented together with results on fabricating micro-channels, cutting optical fiber, fabricating surface relief grating and cylindrical lens. The results demonstrate broad application directions for fabricating telecommunication devices, general optical and photonic components, and biological devices.
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) | 0.002 | 0.001 |
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".