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Record W2750702544 · doi:10.1117/12.2283869

Femtosecond micromachining of glass and semiconductor materials

2017· article· en· W2750702544 on OpenAlexaff
Michael A. Argument

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSurface micromachiningMaterials scienceLaser beam machiningLaserLaser ablationSoda-lime glassLaser drillingSubstrate (aquarium)TungstenOptoelectronicsMachiningExcimer laserOpticsFemtosecondMicrometerComposite materialMetallurgyFabricationDrilling

Abstract

fetched live from OpenAlex

Investigations are being carried out to improve the quality of laser micromachining of glass and semiconductor materials and to achieve submicron finished tolerances. Experiments have been carried out mainly with wavelengths ranging from 248 to 800 nm and pulse lengths of 130 to 400 fs. Comparisons are also being made with machining using 10 ns excimer laser pulses. Laser ablation thresholds, incubation coefficients and ablation rates are measured using single and multiple shot irradiation over a range of incident fluences with well controlled gaussian beams. New techniques for debris removal and crack minimization are being investigated. One technique for debris removal uses a sacrificial thin film of sputtered tungsten on top of the substrate before micromachining. After ablation, the tungsten film and deposited debris may be etched away with hydrogen peroxide. This technique has shown promising results in leaving a much cleaner surface. In order to reduce the amount of cracking of the substrate during laser drilling of glass, we have also been investigating the use of preheated substrates. By raising the temperature of glass before drilling, the sample is more ductile and less prone to cracking.

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.

How this classification was reachedexpand

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 score0.005
Threshold uncertainty score0.296

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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