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Record W4250326587 · doi:10.32920/ryerson.14644920

Femtosecond Laser Machining At Submicron And Nano Scale

2021· preprint· en· W4250326587 on OpenAlexaff
Alireza Dalili

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceLaserFemtosecondMachiningWaferOpticsFluencePulse durationLaser beam machiningOptoelectronicsLaser ablationNano-Composite material

Abstract

fetched live from OpenAlex

The arrival of the femtosecond laser with a MHz repetition rate has provided the industry with a new tool to conduct submicron and nano scale machining. Several advantages such as high quality machining finish, good precision and high throughput can be obtained when using femtosecond laser to conduct nanomachining over lithography techniques currently in use. High repetition rate systems are preferred over low repetition rate femtosecond laser systems that have been studied by others due to their increased stability, speed, quality and discovery of new phenomena such as ripples and grains. This thesis proposes a high repetition rate fiber femtosecond laser system for meeting the above-mentioned conditions. The influence of the laser repetition rate and pulse energy on the size and quality of nano features fabricated on silicon wafers was investigated. Higher repetition rates led to smaller cutlines with uniform width. A 110 nm crater with a small heat affected zone of 0.79 µm was obtained at 13 MHz repetition rate and 2.042 J/cm² energy fluence. In terms of nanomachining below the ablation threshold (surface patterning), the influence of pulse width, repetition rate and pulse energy on the spacing of ripples, as well as diameter of grains created on silicon wafers, was examined. For the pulse width, repetition rate and pulse energy range used, the ripple spacing and grain diameter increased with laser pulse duration while other parameters did not play a significant role. These results show the capability of the proposed system in meeting the industry requirements.

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 categoriesMeta-epidemiology (narrow)
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 score0.037
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.205
Teacher spread0.198 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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