MétaCan
Menu
Back to cohort
Record W2085593019 · doi:10.1088/0960-1317/18/7/075032

A high-repetition-rate femtosecond laser for thin silicon wafer dicing

2008· article· en· W2085593019 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2008
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWafer dicingFemtosecondMaterials scienceWaferLaserSurface micromachiningMachiningLaser beam machiningSiliconOptoelectronicsOpticsFabricationLaser beams

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.183
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it