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Record W2746040748 · doi:10.3762/bjnano.8.176

Laser processing of thin-film multilayer structures: comparison between a 3D thermal model and experimental results

2017· article· en· W2746040748 on OpenAlex
Babak B. Naghshine, Amirkianoosh Kiani

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBeilstein Journal of Nanotechnology · 2017
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsOntario Tech UniversityUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsMaterials scienceThin filmLaserSubstrate (aquarium)Polycrystalline siliconSiliconThin-film transistorOptoelectronicsFabricationNanosecondThermalLaser power scalingOpticsComposite materialNanotechnology

Abstract

fetched live from OpenAlex

In this research, a numerical model is introduced for simulation of laser processing of thin film multilayer structures, to predict the temperature and ablated area for a set of laser parameters including average power and repetition rate. Different thin-films on Si substrate were processed by nanosecond Nd:YAG laser pulses and the experimental and numerical results were compared to each other. The results show that applying a thin film on the surface can completely change the temperature field and vary the shape of the heat affected zone. The findings of this paper can have many potential applications including patterning the cell growth for biomedical applications and controlling the grain size in fabrication of polycrystalline silicon (poly-Si) thin-film transistors (TFTs).

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 score0.063
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.292
Teacher spread0.266 · 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