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Record W3138614672 · doi:10.1515/nanoph-2021-0007

Asymmetric Ge/SiGe coupled quantum well modulators

2021· article· en· W3138614672 on OpenAlexfundno aff
Yi Zhang, Jianfeng Gao, Senbiao Qin, Ming Cheng, Kang Wang, Kai Li, Junqiang Sun

Bibliographic record

VenueNanophotonics · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersWuhan National Laboratory for OptoelectronicsNational Natural Science Foundation of ChinaCollege of Family Physicians of Canada
KeywordsExtinction ratioOptoelectronicsBiasingMaterials scienceModulation (music)PhotonicsSilicon photonicsElectro-absorption modulatorBandwidth (computing)VoltageOptical modulatorWaveguideQuantum wellPhysicsOpticsPhase modulationQuantum dot laserSemiconductorComputer scienceTelecommunicationsSemiconductor laser theory

Abstract

fetched live from OpenAlex

Abstract We design and demonstrate an asymmetric Ge/SiGe coupled quantum well (CQW) waveguide modulator for both intensity and phase modulation with a low bias voltage in silicon photonic integration. The asymmetric CQWs consisting of two quantum wells with different widths are employed as the active region to enhance the electro‐optical characteristics of the device by controlling the coupling of the wave functions. The fabricated device can realize 5 dB extinction ratio at 1446 nm and 1.4 × 10 −3 electrorefractive index variation at 1530 nm with the associated modulation efficiency V π L π of 0.055 V cm under 1 V reverse bias. The 3 dB bandwidth for high frequency response is 27 GHz under 1 V bias and the energy consumption per bit is less than 100 fJ/bit. The proposed device offers a pathway towards a low voltage, low energy consumption, high speed and compact modulator for silicon photonic integrated devices, as well as opens possibilities for achieving advanced modulation format in a more compact and simple frame.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.955

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)0.0000.001

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations10
Published2021
Admission routes1
Has abstractyes

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