Quantized structuring of transparent films with femtosecond laser interference
Bibliographic record
Abstract
The confinement of laser interactions inside transparent materials assisted by tight optical focusing and short-pulsed nonlinear interactions has driven many high-resolution patterning and probing applications in science and technology. In thin transparent films, laser interactions confined to the film/substrate interface have underpinned blistering and ejection processes for nanofluidic channel fabrication, film patterning and cell catapulting. Here, we harness femtosecond lasers to drive nonlinear interactions within Fabry–Perot interference fringes to define narrow nanolength scale zones for highly resolved internal structuring of a film of refractive index, nfilm, at fringe maxima separated by λ/2nfilm. This novel interaction internally cleaves the film to open subwavelength internal cavities and form thin membranes at single or multiple depths from which follow significant opportunities for writing multilevel nanofluidic channels inside the film, as well as ejecting nanodisks at quantized film depths for coloring and three-dimensional surface patterning that promise new compact types of lab-in-film devices. High-resolution structuring of thin transparent films using a femtosecond laser can aid the fabrication of various devices. Kitty Kumar and co-workers from the University of Toronto in Canada say that interference from reflections at the film’s boundaries causes the laser light to create a series of periodic plasma disks in the dielectric film that eject material. The resulting laser-generated nanovoids could be useful for fabricating miniature cavities and fluidic waveguides or performing nanoscale texturing to improve light harvesting in solar cells or light extraction from LEDs. The researchers tested their scheme with a stream of 200 fs pulses from a 522 nm frequency-doubled fibre laser operating at a repetition rate of 100 kHz. They focused the pulses onto an SiNx film measuring 20–1,545 nm thick.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".