Inhibition and Enhancement of Quantized, Interference‐Driven, Ultrafast‐Laser Cleaving, and Intrafilm Ejection with Angle and Polarization Control
Bibliographic record
Abstract
Abstract The combination of optical interference and ultrafast laser interaction within microscale transparent films offers novel high resolution means for nonlinear confinement of dissipated energy to facilitate 3D nanostructuring. This approach relies on the formation of nanoscale (≈40 nm) plasma disks stacked on half‐wavelength spacings, λ/2nfilm (film refractive index, nfilm), opening directions for intrafilm cleaving and nanostructuring of free‐standing blisters or embedded nanocavities with controllable surface topography. Given a limited number of film‐substrate systems suitable for generating high contrast interference fringes, this paper introduces angle and polarization control to manipulate fringe visibility in SiOx thin‐films (1 µm thickness) with silicon substrates. An enhancement or diminishment of quantized intrafilm processing is definitively demonstrated according to s‐ and p‐polarization states, respectively. Modeling of Gaussian beam walk‐off effects further explores film interference in tight focusing limits, predicting new asymmetry that manipulates intrafilm cleaving morphology. This research opens a path to quantized structuring of previously unsuitable low‐contrast thin‐film systems, while improving the design and control over novel surface and intrafilm morphologies. The development of intrafilm structuring in SiOx thin‐films is relevant to lab‐in‐film opportunities for assessing cell or subcellular species in CMOS‐compatible microelectronics and improving functionality of LED, lab‐on‐a‐chip and MEMS devices.
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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) | 0.001 | 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".