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Record W92033795

In situ observation of laser induced crystallisation in group IV semiconductors.

2014· article· en· W92033795 on OpenAlexfundno aff
L. Nikolova

Bibliographic record

VenueEspaceINRS (National Institute for Scientific Research (Canada)) · 2014
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
FundersLawrence Livermore National LaboratoryFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNucleationCrystallizationMaterials scienceMetastabilityPhase (matter)Chemical physicsCrystallographyNanocrystalline materialTransmission electron microscopyMicrostructureSemiconductorChemical engineeringThermodynamicsNanotechnologyChemistryComposite materialOptoelectronicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

The main objectives of this thesis were to study in situ the structure formation during
\nlaser induced crystallisation in group IV semiconductors. We were interested to reveal the
\nsequence of change in the morphology during the growth of the different microstructures,
\nto unveil the shape of the growth front during its propagation, to estimate the velocities of
\ncrystallisation and to shed light on the possible mechanisms of growth through estimation
\nof the temperature within the films. For that, we used Dynamic Transmission Electron
\nMicroscopy (DTEM) because the technique offers the required combination of spatial and
\ntemporal resolutions for in situ visualisation of the phase transformation. We used freestanding
\nand membrane-supported films with the goal of elucidating the influence of the
\nsupporting layer on the kinetics and thermodynamics of the phase transformation.
\nIn the first part of this work, we studied the crystallisation dynamics in membrane-
\nSupported and self-sustained a-Ge films. We acquired time resolved images of the
\nformation of the central nanocrystalline structure, the evolution of the size and the
\nroughness of the crystallisation front during the two other structures growth i.e. dendritic
\nand layered structure. This study allowed us to estimate the velocities of crystallisation and
\nthe evolution of the temperature within the film. We found that in the central region,
\nnucleation from the bulk occurs at temperatures below the melting temperature of the
\ncrystalline material. We showed that the dendritic growth occurs by propagation of thin
\nmetastable liquid layer in front of the growing crystalline phase and is a result of high
\nthermal instabilities at the growth front. We demonstrated that the growth of the layered
\nstructure takes place at temperature below the melting temperature of the crystalline
\nmaterial and proceeds in azimuthal direction, i.e. orthogonal to the direction of the net heat
\nflow. However, the growth mechanisms remains similar to the dendritic growth because
\nthe heat diffusion can allow formation of very thin pocket-like layer to be formed and
\npropagate III the azimuthal direction. These findings allowed us to overturn earlier
\nhypothesis based solely on the post-mortem equilibrium investigation of the structure.
\nIn the second part of the thesis we studied the crystallisation dynamics in amorphous
\nsilicon. We showed that the formed structure is dependent on the fluence of the pump-Iaser.
\nWe demonstrated that at the fluences used in this study the process is melt-mediated
\nfavoring super-lateral growth. Nanocrystallization occurs at the periphery of the melted
\nzone but with very limited extent. The crystallisation proceeds inward i.e. toward the center
\nof the heated region resulting in the observed long-grained microstructure. Our observations
\nare consistent with the earlier developed theory on super-lateral growth where the
\nnucleation and nanocrystallisation occurs at the periphery of the heated region and the
\nelongated grains are growing along the heat gradient i.e. toward the central part of the
\nheated region. Our study contributed for better understanding of growth of these structures
\nand permitted estimation of the evolution of the temperature within the film.

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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)0.003
metaresearch head score (Gemma)0.001
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 score0.826
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.056
GPT teacher head0.301
Teacher spread0.246 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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