MétaCan
Menu
Back to cohort
Record W2108736232 · doi:10.1109/jqe.2005.848891

Frequency response measurements for mirror image asymmetric multiple quantum-well lasers

2005· article· en· W2108736232 on OpenAlexaff
Aaron D. Vandermeer, Daniel T. Cassidy

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2005
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLaserResonance (particle physics)Quantum wellPosition (finance)OpticsSemiconductor laser theoryPhysicsFrequency responseOptoelectronicsAtomic physicsElectrical engineering

Abstract

fetched live from OpenAlex

A comparison of measurements of the frequency response characteristics of mirror image asymmetric multiple quantum-well (AMQW) lasers is presented in this paper. Conclusions on the carrier transport were drawn from these measurements, which are significant for the design of both AMQW and standard devices. The effective resonance frequency trend with current derived from these measurements shows no significant difference for several mirror image structures. It is thought that this is because of a very fast inter-well transport time in these devices. There was also no significant difference in the temperature dependence of the effective resonance frequency for these mirror image structures. This indicates that temperature-dependent transport mechanisms such as carrier capture and escape have little effect on the resonance frequency of these structures. These trends provide support for an analytic equation for the resonance frequency of AMQW structures that is independent of well position and carrier transport times. These results are important for designing AMQW lasers for high-speed applications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Journal of Quantum ElectronicsSame topicSemiconductor Lasers and Optical DevicesFrench-language works237,207