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Record W2160006461 · doi:10.1109/jphot.2010.2049831

Extreme Miniaturization of Silicon Add–Drop Microring Filters for VLSI Photonics Applications

2010· article· en· W2160006461 on OpenAlex

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

VenueIEEE photonics journal · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSilicon on insulatorResonatorPhotonicsMiniaturizationFree spectral rangeSilicon photonicsSiliconInsertion lossOptoelectronicsLimit (mathematics)Materials scienceOpticsPhysicsNanotechnology

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We theoretically and experimentally investigated the performance of silicon-on-insulator (SOI) microring add–drop filters in the limit of extreme miniaturization for potential application in very dense integration of silicon photonic devices. Rigorous numerical analyses were performed to predict the theoretical limit of achievable intrinsic quality factors as the microring radius is scaled down to 1 <formula formulatype="inline"><tex Notation="TeX"> $\mu\hbox{m}$</tex></formula>. Experimental measurements of fabricated SOI microring resonators showed that ultracompact add–drop microring filters with radii as small as 1 <formula formulatype="inline"><tex Notation="TeX">$\mu \hbox{m}$</tex></formula> can be achieved with a free spectral range exceeding 80 nm and an insertion loss of only 1 dB. These devices are also shown to exhibit intrinsic quality factors approaching the theoretically achievable limit set by the bending loss in ultracompact microring resonators. </para>

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.880

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 integrity0.0000.001
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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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