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Record W2084029864 · doi:10.1364/oe.23.001073

Excimer laser induced quantum well intermixing: a reproducibility study of the process for fabrication of photonic integrated devices

2015· article· en· W2084029864 on OpenAlexafffund
Romain Béal, Vincent Aimez, Jan J. Dubowski

Bibliographic record

VenueOptics Express · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité de SherbrookeCMC Microsystems
KeywordsMaterials scienceOptoelectronicsFabricationLaserWaferPhotonicsReproducibilityPhotolithographyExcimer laserOpticsPhotoluminescenceHeterojunctionChemistryPhysics

Abstract

fetched live from OpenAlex

Excimer (ultraviolet) laser-induced quantum well intermixing (UV-Laser-QWI) is an attractive technique for wafer level post-growth processing and fabrication of a variety of monolithically integrated photonic devices. The results of UV-Laser-QWI employed for the fabrication of multibandgap III-V semiconductor wafers have demonstrated the attractive character of this approach although the process accuracy and reproducibility have remained relatively weakly covered in related literature. We report on a systematic investigation of the reproducibility of this process induced with a KrF excimer laser. The influence of both the irradiation with different laser doses and the annealing temperatures on the amplitude of intermixing in InGaAs/InGaAsP/InP quantum well heterostructures has been evaluated based on the photoluminescence measurements. Under optimized conditions, the process allows to blue shift the bandgap of a heterostructure by more than 100 nm with a remarkable 5.3% relative standard deviation.

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)0.001
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 score0.204
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.049
GPT teacher head0.297
Teacher spread0.248 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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