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Record W2092010855 · doi:10.1117/12.406366

Quantum dot intermixing with thermal and laser annealing

2000· article· en· W2092010855 on OpenAlexaff
Claudine Nì. Allen, Jan J. Dubowski, P. G. Piva, Simon Fafard

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAnnealing (glass)PhotoluminescenceMaterials scienceQuantum dotLaserFull width at half maximumOptoelectronicsMolecular physicsAtomic physicsOpticsPhysics

Abstract

fetched live from OpenAlex

Photoluminescence (PL) was used to investigate the interdifflision of self-assembled InAs/GaAs quantum dots (QDs) treated by rapid thermal annealing (RTA) and laser annealing. The observation ofintense and sharp shell structures confirmed that the QDs retained their zero-dimensional density of states. In addition, three main effects of alloy intermixing were demonstrated in QDs having different intersublevel spacings. The emission has been strongly blue-shifted, up to -200 meV for RTA samples and 298 meV for the laser annealed ones. The intersublevel spacing was tuned between 6O meV to '-'25 meV in the RTA case, but down to -12 meV in the case of laser-induced intermixing. Finally the inhomogeneous broadening linearly decreased from a FWHM of-46 meV down to smaller than 15 meV for RTA and 8 meV in the most extreme case of laser annealing. For samples annealed at the highest temperatures, the most energetic shells of QDs become unbound. Across varying samples, the result ofthe intermixing was to increase the uniformity of their PL spectra. A onedimensional model of Fickian diffusion for the growth direction was used to model their PL emission. Rapid thermal annealing and laser annealing provide two additional ways of manipulating the energy levels of self-assembled QD ensembles by tuning the intersublevel energy-spacing and the number ofconfined states.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.801

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 head0.009
GPT teacher head0.220
Teacher spread0.212 · 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
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSemiconductor Quantum Structures and DevicesFrench-language works237,207