Femtosecond Laser Machining of an X-ray Mask in a 500 Micron-Thick Tungsten Sheet
Bibliographic record
Abstract
Femtosecond laser material processing (FLMP) was used to make an X-ray mask in a 500 µm thick tungsten sheet without the use of any chemical etch methods. The laser produced an 800 nm wavelength at a 1 kHz repetition rate and a pulse width of 100 fs. The laser beam arrival at the tungsten sheet was synchronized to a computer numerically controlled (CNC) stage that allowed for motion in the XYZθ directions. The X-ray mask design was made using CAD/CAM software (Alphacam 2019 R1) and it consisted of linear, circular, and 45° angle features that covered an area of 10 mm × 10 mm. A total of 70 laser beam passes at a moderate laser energy of 605.94 J/cm2 were used to make through-cut features into the tungsten sheet. The morphology of the top view (laser incident, LS) images showed cleaner and smoother cut edges relative to the bottom view (laser exit, LE) images. It was found that the size dimensions of the through-cut features on the LE surfaces were better aligned with the CAD dimensions than those of the LS surfaces. The focused laser beam produced inclined cut surfaces as the beam made the through cut from the LS to the LE of the tungsten sheet. The circular features at the LS surface deviated toward being oval-like on the LE surface, which could be compensated for in future CAD designs. The dependence of the CNC processing speed on the thickness of the etch depth was determined to have a third-order exponential decay relationship, thereby producing a theoretical model that will be useful for future investigators to predict the required experimental parameters needed to achieve a known etch depth in tungsten. This is the first study that has demonstrated the capability of using a femtosecond laser to machine through-cut an X-ray mask in a 500 µm thick tungsten sheet with no involvement of a wet etch or any other such supporting process.
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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 | 0.000 | 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) | 0.000 | 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".