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Record W4386022479 · doi:10.1364/ol.500240

Short, broadband, and polarization-insensitive adiabatic Y-junction power splitters

2023· article· en· W4386022479 on OpenAlex
Can Özcan, Mo Mojahedi, J. Stewart Aitchison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOptics Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpticsBroadbandPolarization (electrochemistry)Adiabatic processPhysicsBeam splitterMaterials scienceOptoelectronicsLaser

Abstract

fetched live from OpenAlex

Adiabatic Y-junction power splitters have low loss, large bandwidth, high polarization insensitivity, and high tolerance to fabrication errors. However, the adiabatic transition lengths required are generally much longer than other power splitters. Using a nonlinear taper profile can considerably shorten the device length. Here, we introduce a taper profile optimization algorithm based on polynomial functions, which significantly reduces the lengths of the adiabatic power splitters without increasing losses. We experimentally demonstrate the performance of the adiabatic power splitters for minimum feature sizes of 80 nm, 120 nm, and 160 nm on the 220 nm silicon-on-insulator (SOI) platform. Our best device has a minimum feature size of 120 nm and a length of 14 µm, with measured losses of 0.25 dB and 0.23 dB for the transverse electric (TE) and transverse magnetic (TM) modes, respectively, in the 1500-1600 nm region. This device has an average transmission of -3 ± 0.5 dB in the 1500-1600 nm region, indicating highly balanced splitting over a large spectral range.

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.

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 score0.663
Threshold uncertainty score0.677

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 head0.009
GPT teacher head0.200
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