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Record W2556604059 · doi:10.1021/acsphotonics.6b00562

Strained Pseudomorphic Ge<sub>1–<i>x</i></sub>Sn<sub><i>x</i></sub> Multiple Quantum Well Microdisk Using SiN<sub><i>y</i></sub> Stressor Layer

2016· article· en· W2556604059 on OpenAlexfundno aff
Colleen S. Fenrich, Xiaochi Chen, Robert Chen, Yi‐Chiau Huang, Hua Chung, Ming-Yen Kao, Yijie Huo, T. I. Kamins, James S. Harris

Bibliographic record

VenueACS Photonics · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersDivision of Electrical, Communications and Cyber SystemsNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsMaterials sciencePhotoluminescenceStrain (injury)Raman spectroscopyQuantum wellBand gapDeformation (meteorology)OptoelectronicsEpitaxyCondensed matter physicsLayer (electronics)OpticsComposite materialPhysicsLaser

Abstract

fetched live from OpenAlex

We demonstrate tensile-strained pseudomorphic Ge 0.934 Sn 0.066 /Ge quantum wells in a microdisk resonator using silicon nitride stressor layers. The hydrostatic and biaxial strain distributions are studied through finite element modeling, while confocal Raman spectroscopy shows local biaxial strain transfers as high as 1.1% at freestanding microdisk edges. These strains are sufficient to overcome the original compressive strain in Ge 0.934 Sn 0.066 epitaxy and reach a direct band gap according to deformation potential theory. A red-shift in microdisk photoluminescence confirms the reduced band gap energies in response to tensile strain and suggests an average biaxial strain transfer of 0.55%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2016
Admission routes1
Has abstractyes

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