Single-Shot Real-Time Ultrafast Imaging of Femtosecond Laser Fabrication
Bibliographic record
Abstract
Femtosecond laser fabrication outperforms the traditional fabrication techniques with high precision, high efficiency, low collateral damage and wide applicability, which has shown to be a powerful tool in precision machining. Imaging the ultrafast dynamics of femtosecond laser fabrication is necessary for understanding the processing mechanism and for establishing the corresponding physical models. Up to now, ultrafast measurement techniques based on the pump–probe strategy are the most used methods. However, they are limited by laser energy stability and materials surface uniformity, which have a heavy impact on the dynamic measurement precision of femtosecond laser fabrication. To overcome this limitation of the traditional pump–probe techniques, we developed chirped spectral mapping ultrafast photography (CSMUP), which can achieve single-shot real-time ultrafast imaging with a frame rate of about 250 billion frames per second (temporal frame interval of 4 ps) and a spatial resolution of less than 833 nm. We experimentally imaged the dynamics of femtosecond laser ablation in silicon under a 400 nm femtosecond laser exposure with CSMUP, and the experimental result agreed well with previous theoretical models. CSMUP provides a new strategy to improve the efficiency and accuracy of femtosecond laser fabrication by a single-shot dynamic measurement of the interaction between the femtosecond laser and materials, and it is expected to work as a real-time detection method for various ultrafast phenomena.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".