Micromachining with femtosecond 250-nm laser pulses
Bibliographic record
Abstract
Laser micromachining is a flexible technique for precision patterning of surfaces in microelectronics, microelectromechanical devices and integrated optical devices. Typical applications include drilling of holes, cutting of conducting lines or shaping of micro component surfaces. The resolution, edge finish and residual damage to the surrounding and underlying structures depend on a variety of parameters including laser energy, intensity, pulse width and wavelength. Femtosecond pulses are of particular interest because the limited time of interaction limits the lateral expansion of the plasma and the inward propagation of the heat front. Thus, very small spot size can be achieved and minimal heating and damage of underlying layers can be obtained. An additional advantage of femtosecond pulses is that multiphoton absorption leads to efficient coupling of energy to many materials independent of the linear reflectivity of the surface. Thus metals and transmitting dielectrics, which are difficult to micromachine, may be machined with such pulses. The coupling is improved further by employing ultraviolet wavelength laser pulses where the linear absorption typically is much higher than for visible and infrared laser pulses. To explore these advantages, we have initiated a study of the interaction of 250nm femtosecond laser pulses with metals. The laser pulses are obtained by generating the third harmonic from a femtosecond Ti:sapphire laser operating at 750nm. The pulses are focused to various intensities in the range of 1010Wcm2 to 1015 Wcm2 using reflective and refractive microscope objectives and ablation thresholds and ablation rates have been determined for a few metals. In addition the ability to control feature size and produce submicron holes and lines have been investigated. The results are presented and compared to results obtained using infrared and visible femtosecond laser pulses.
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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 | 0.001 |
| 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".