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Record W4392198347 · doi:10.3390/photonics11030212

On-Chip Lasers for Silicon Photonics

2024· article· en· W4392198347 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhotonics · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsOptoelectronicsPhotonicsLaserSilicon photonicsSiliconMaterials scienceChipSilicon chipOpticsSemiconductor laser theoryComputer scienceTelecommunicationsPhysicsSemiconductor

Abstract

fetched live from OpenAlex

With the growing trend in the information industry, silicon photonics technology has been explored in both academia and industry and utilized for high-bandwidth data transmission. Thanks to the benefits of silicon, such as high refractive index contrast with its oxides, low loss, substantial thermal–optical effect, and compatibility with CMOS, a range of passive and active photonic devices have been demonstrated, including waveguides, modulators, photodetectors, and lasers. The most challenging aspect remains to be the on-chip laser source, whose performance is constrained by the indirect bandgap of silicon. This review paper highlights the advancements made in the field of integrated laser sources on the silicon photonics platform. These on-chip lasers are classified according to their gain media, including V semiconductors, III–V semiconductors, two-dimensional materials, and colloidal quantum dots. The methods of integrating these lasers onto silicon are also detailed in this review.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.854

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 head0.013
GPT teacher head0.242
Teacher spread0.230 · 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