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Record W3210798135 · doi:10.5539/mas.v15n6p27

Three Cases of Gradual Degradation Mode Analysis of Semiconductor Laser Diodes

2021· article· en· W3210798135 on OpenAlexvenueno aff
Jack Jia‐Sheng Huang, Churou Wang, Yu‐Heng Jan

Bibliographic record

VenueModern Applied Science · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Degradation (telecommunications)DiodeLaserSemiconductor laser theoryMaterials sciencePower (physics)Reliability engineeringSemiconductorComputer scienceFailure rateLaser diodeAerospaceMode (computer interface)OptoelectronicsElectronic engineeringOpticsTelecommunicationsPhysicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Semiconductor laser diodes are important components for various applications such as 5G wireless, datacenter, passive optical network, and aerospace applications. High reliability has emerged to be the universal requirement for all optical applications. To achieve high reliability, fundamental understanding of the laser degradation behavior is crucial. In this paper, we study three cases of gradual degradataion modes of laser diodes including (1) Pattern-A that is associated with threshold current change only, (2) Pattern-B that involve both threshold current and power changes, and (3) Pattern-C that is associated with merely power change. We have instituted reliability equations for the degradation processes. The new reliability models could provide estimation on the laser end-of-life based on the degradation rate and device performance specification.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.026
GPT teacher head0.252
Teacher spread0.226 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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