A femtosecond laser-induced periodical surface structure on crystalline silicon
Bibliographic record
Abstract
A laser-induced periodic surface structure (LIPSS) has attracted research interest for its promising potential in micromachining for microelectronics and microelectromechanical systems. A femtosecond laser-induced periodical surface structure was investigated for polished crystalline silicon. The observed structure is similar to the classical ripples that are characterized by long, nearly parallel lines extending over the entire irradiated area on the metal and silicon surface after continuous or pulsed laser irradiation. The spacing of the ripples nearly equals the irradiation wavelength. The depth of these ripples increases nonlinearly with the fluence of irradiation. The orientation of these periodic structures is perpendicular to the vector of the electric field of the laser beam. It seemed that the pattern formed by a femtosecond laser complies well with conventional models. Unlike the patterns formed by a continuous or nanosecond pulsed laser, however, the spacing of the ripple formed by femtosecond pulses is not influenced by the incident angle of the laser beam. The formula used to predict the ripple spacing in the conventional model does not apply to the femtosecond laser induced ripple structure. A plausible explanation to this phenomenon is proposed. The effect of the pulse repetition rate was studied and it was found that a femtosecond laser oscillator generates the same periodic structure as the amplified laser system.
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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".