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Record W4390502168 · doi:10.1364/oe.510036

On-chip hybrid integration of swept frequency distributed-feedback laser with silicon photonic circuits using photonic wire bonding

2024· article· en· W4390502168 on OpenAlexaff
Sheri Jahan Chowdhury, Kithmin Wickremasinghe, Samantha M. Grist, Hang Zou, Matthew Mitchell, Mohammed Al-Qadasi, Becky Lin, Davin Birdi, Shannon Nicole Smythe, Sudip Shekhar, Karen C. Cheung, Lukas Chrostowski

Bibliographic record

VenueOptics Express · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhotonic integrated circuitMaterials sciencePhotonicsOptoelectronicsSilicon on insulatorWire bondingSilicon photonicsLaserResonatorElectronic circuitSemiconductor laser theoryHybrid silicon laserIntegrated circuitChipOpticsSiliconSemiconductorComputer scienceElectrical engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper presents a novel co-packaging approach through on-chip hybrid laser integration with photonic circuits using photonic wire bonding. The process involves die-bonding a low-cost semiconductor distributed-feedback (DFB) laser into a deep trench on a silicon-on-insulator (SOI) chip and coupling it to the silicon circuitry through photonic wire bonding (PWB). After characterizing the power-current-voltage (LIV) and optical spectrum of the laser, a wavelength-current relationship utilizing its tunability through self-heating a swept-frequency laser (SFL) is developed. Photonic integrated circuit (PIC) resonators are successfully characterized using the SFL method, demonstrating signal detection with a quality factor comparable to measurements conducted with an off-chip benchtop laser.

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 categoriesMeta-epidemiology (narrow)
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 score0.472
Threshold uncertainty score1.000

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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.

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

Citations15
Published2024
Admission routes1
Has abstractyes

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