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Record W3156114439 · doi:10.1109/lpt.2021.3072787

Adiabatic Coupler With Nonlinearly Tapered Mode-Evolution Region

2021· article· en· W3156114439 on OpenAlexaff
Mao Deng, Yun Wang, Luhua Xu, Eslam El‐Fiky, Maxime Jacques, Jinsong Zhang, Md Samiul Alam, Amar Kumar, Yannick D’Mello, David V. Plant

Bibliographic record

VenueIEEE Photonics Technology Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsTaperingAdiabatic processMaterials scienceAstronomical interferometerQuadratic equationOpticsOptoelectronicsPhysicsInterferometryMathematicsComputer scienceGeometry

Abstract

fetched live from OpenAlex

In this work, we propose a time-efficient method to analyze and design the adiabatic couplers (ACs) on the silicon-on-insulator platform. By analyzing the mode-evolution region of ACs, we derive the boundary conditions and necessary constraints on the tapering functions to achieve optimized performance. Taking these conditions into consideration, we choose three common types of functions for the mode-evolution region. Based on the width and separation of the constituent waveguide pair, the performance of ACs with different tapering functions is compared. The compared ACs were fabricated and measured. The splitting ratios (SRs) of the fabricated devices are characterized using unbalanced Mach-Zehnder interferometers. We analytically and experimentally prove that, for a designed 3-dB AC, a quadratic separation and exponentially varying width provides the least SR imbalance with the smallest footprint among the compared tapering methods. The extracted SRs of the designed 3-dB AC using such tapering method are between 47%/53% from 1500 nm to 1600 nm with a mode evolution length of 110 μm. Using this tapering method, we also experimentally demonstrate imbalanced ACs with SRs of 8%/92%, 12%/88%, 15%/85%, 23%/77%, 30%/70%, and 42%/58% measured at 1550 nm.

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 score0.617
Threshold uncertainty score0.916

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 integrity0.0000.001
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 head0.196
Teacher spread0.190 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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