Optical coherence and beamspread in ultrafast-laser pulsetrain-burst hole drilling
Bibliographic record
Abstract
Pulsetrain-burst machining has been shown to have advantages over single-pulse laser processing of materials and biological tissues. Ultrafast lasers are often able to drill holes in brittle and other difficult materials without cracking or swelling the target material, as is sometimes the case for nanosecond-pulse ablation; further, pulsetrain-bursts of ultrafast pulses are able to recondition the material during processing for instance, making brittle materials more ductile and striking advantages can result. In the work we report, we have investigated hole-drilling characteristics in metal and glass, using a Nd:glass pulsetrain-burst laser (1054 nm) delivering 1-10 ps pulses at 133 MHz, with trains 3-15 μs long. We show that as the beam propagates down the channel being drilled, the beam loses transverse coherence, and that this affects the etch-rate and characteristics of channel shape: as the original Gaussian beam travels into the channel, new boundary conditions are imposed on the propagating beam principally the boundary conditions of a cylindrical channel, and also the effects of plasma generated at the walls as the aluminum is ablated. As a result, the beam will decompose over the dispersive waveguide modes, and this will affect the transverse coherence of the beam as it propagates, ultimately limiting the maximum depth that laser-etching can reach. To measure transverse beam coherence, we use a Youngs two-slit interference setup. By measuring the fringe visibility for various slit separations, we can extract the transverse coherence as a function of displacement across the beam. However, this requires many data runs for different slit separations. Our solution to this problem is a novel approach to transverse coherence measurements: a modified Michelson interferometer. Flipping the beam left-right on one arm, we can interfere the beam with its own mirror-image and characterise the transverse coherence across the beam in a single shot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".