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Crosstalk Reduction Enabled by Hybrid Metallo-Dielectric Waveguide Architecture

2024· article· en· W4403938028 on OpenAlexaff
Chunmei Liu, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCrosstalkDielectricReduction (mathematics)OptoelectronicsMaterials scienceArchitectureWaveguideComputer scienceElectronic engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Although substrate-integrated non-radiative dielectric (SINRD) waveguides have demonstrated an excellent potential for THz applications, they suffer from a high level of crosstalk when two or more paths intersect with each, which is inevitable in integrated circuits and systems. In this work, hybrid metallo-dielectric waveguide (HMDW) architecture is used to reduce such crosstalk of intersecting guides. Metal layers outside the intersection of two SINRD waveguides are removed to create substrate-integrated dielectric waveguide (SIDW), thus forming a mode-selective interface to suppress certain undesired mode in connection with crosstalk. Moreover, leveraging SIDW beyond the intersection helps reduce conductor loss. Simulation shows that a 6.1 mm back-to-back substrate-integrated hybrid metallo-dielectric (SIHMD) waveguide with six identical intersections has an average insertion loss of 3.5 dB, whereas the SINRD counterpart has that of 7 dB over the frequency band from 268 GHz to 296 GHz. Simple geometric feature and metallized-viafree manufacturing process of the proposed topology for reducing crosstalk are set to significantly relax manufacturing requirements in THz.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.788

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)0.0010.000

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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