A high-repetition-rate femtosecond laser for thin silicon wafer dicing
Bibliographic record
Abstract
In this study, a high-power–high-repetition-rate femtosecond laser was investigated for singulation of silicon wafers. The femtosecond laser used for this investigation, unlike the previously used amplified system, is a compact unit that emits infrared ultrashort pulses at high repetition rates in the MHz range and an average output power of 11 W. A systematic study of the influence of the laser parameters on the kerf width, depth and quality of machining was carried out. A number of different experiments were performed using a silicon wafer of diameter 50 mm, P-type boron doped and back grinded to a 250 µm thickness wafer with orientation of ⟨1 0 0⟩. The experimental results show that the high-power–high-repetition-rate femtosecond laser can be a promising and competitive tool for thin wafer dicing. It is also the first time that the high-repetition-rate femtosecond laser has been demonstrated for real-world industrial applications for micromachining. A cutting speed of 40 mm s −1 with acceptable quality of sidewalls, depth of cut and kerf width was demonstrated during the experiment which can be considered when applying for industrial usage.
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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 | 0.001 | 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".