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Record W2111797268 · doi:10.1088/0957-4484/23/6/065603

Modified Stranski–Krastanov growth in Ge/Si heterostructures via nanostenciled pulsed laser deposition

2012· article· en· W2111797268 on OpenAlexafffund
Jennifer MacLeod, C. V. Cojocaru, Fulvio Ratto, Cătălin Harnagea, Alessandro Bernardi, M. I. Alonso, Federico Rosei

Bibliographic record

VenueNanotechnology · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials sciencePulsed laser depositionHeterojunctionRaman spectroscopyWetting layerSubstrate (aquarium)Scanning electron microscopeDeposition (geology)LaserRelaxation (psychology)OptoelectronicsThin filmAnalytical Chemistry (journal)NanotechnologyLayer (electronics)OpticsComposite materialChemistry

Abstract

fetched live from OpenAlex

The combination of nanostenciling with pulsed laser deposition (PLD) provides a flexible, fast approach for patterning the growth of Ge on Si. Within each stencilled site, the morphological evolution of the Ge structures with deposition follows a modified Stranski-Krastanov (SK) growth mode. By systematically varying the PLD parameters (laser repetition rate and number of pulses) on two different substrate orientations (111 and 100), we have observed corresponding changes in growth morphology, strain and elemental composition using scanning electron microscopy, atomic force microscopy and μ-Raman spectroscopy. The growth behaviour is well predicted within a classical SK scheme, although the Si(100) growth exhibits significant relaxation and ripening with increasing coverage. Other novel aspects of the growth include the increased thickness of the wetting layer and the kinetic control of Si/Ge intermixing via the PLD repetition rate.

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 score0.112
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 science0.0000.000
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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