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Record W4323843785 · doi:10.3390/mi14030633

Enhanced Modulation Bandwidth by Delayed Push–Pull Modulated DFB Lasers

2023· article· en· W4323843785 on OpenAlexaff
Jiewen Chi, Xun Li, Chuanning Niu, Jia Zhao

Bibliographic record

VenueMicromachines · 2023
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBandwidth (computing)BottleneckLaserOpticsDistributed feedback laserResonance (particle physics)OptoelectronicsModulation (music)Materials scienceFrequency modulationPhysicsComputer scienceTelecommunicationsAcoustics

Abstract

fetched live from OpenAlex

The bandwidth of a distributed feedback (DFB) directly modulated laser (DML) is limited by its carrier-photon resonance (CPR) frequency. A viable approach to break the bottleneck is to introduce a photon-photon resonance (PPR), since the PPR can happen at a much higher frequency than the CPR. Among the many structures that can possibly generate the PPR, the dual-sectional push-pull modulated (PPM) DFB is of particular interest for its fabrication cost-effectiveness as no regrowth is required. The PPR in the PPM DFB, however, usually shows a rapid roll-off on both edges, which brings in an indentation on the lower frequency side of the PPR peak and, consequently, cuts off the bandwidth. To compensate for this dip, we introduce a detuned PPR and restart the CPR response by exploiting a time delay between the differential signals applied to the PPM DFB. Our simulation result shows that the broadened PPR peak and the restarted CPR response indeed mitigate the dip and effectively expand the PPM-DFB's bandwidth to approximately 50 GHz, a value double that of the conventional (single-sectional) DFB DML.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.807

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 teacher head, 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

Citations5
Published2023
Admission routes1
Has abstractyes

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