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Record W2253032726 · doi:10.5539/apr.v8n1p149

Defect Diffusion Model of InGaAs/InP Semiconductor Laser Degradation

2016· article· en· W2253032726 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Physics Research · 2016
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceCladding (metalworking)OptoelectronicsLaserIndium gallium arsenideSemiconductor laser theoryDiodeDiffusionDegradation (telecommunications)Reliability (semiconductor)SemiconductorOpticsGallium arsenideComputer sciencePhysicsTelecommunicationsComposite material

Abstract

fetched live from OpenAlex

<p class="1Body">To enable high-performance fiber to the x (FTTx) and datacenter networks, it is important to achieve reliable and stable optical components over time. Laser diode is the essential building block of the optical components. Degradation analysis is critical for overall successful reliability design. In this paper, we study the modelling and experimental data of the InGaAs/InP laser degradation. We present a defect diffusion model that involves three propagation media (p-InGaAs contact, p-InP cladding and multi-quantum wells). We propose a simple constitutive equation based on the Gauss error function to describe the defect propagation. The physical model assumes that the p-InGaAs is the rate-limiting factor for the defect diffusion process.</p>

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.064
GPT teacher head0.285
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