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Record W2523451441 · doi:10.1109/isemc.2016.7571705

Millimeter-wave broadband transition of stripline and CPWG on thin-to-thick substrates

2016· article· en· W2523451441 on OpenAlexaff
Nasser Ghassemi, Hugues Tournier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCiena (Canada)
Fundersnot available
KeywordsStriplineMaterials scienceInsertion lossBroadbandOptoelectronicsCoplanar waveguideSubstrate (aquarium)Extremely high frequencyReturn lossElectrical engineeringAntenna (radio)TelecommunicationsMicrowaveEngineering

Abstract

fetched live from OpenAlex

In many applications such as chip (or package) to PCB transition, due to the small pitch size of the chip (or package), very narrow 50 Ω; stripline or grounded coplanar waveguide (CPWG) is required. In order to reduce the line width, stripline or CPWG should be fabricated on thin substrate (in case of CPWG because minimum manufacturable gap is limited in normal low cost PCB technology). On the other hand using thin substrate increases metallic loss of CPWG and stripline, which is important especially at millimeter wave, and long channels, then it, is preferred to use thick substrate to reduce metallic loss. Also for many applications such as printed circuit antenna, thick substrate is preferred because it can increase the bandwidth and gain of the antenna. Then a broadband transition from stripline (or CPWG) on thin to thick substrate is needed. This paper presents a low-loss, broadband transition of stripline (and CPWG) on thin-to-thick substrates. Measurement results show that the proposed transitions have less than 0.1 dB insertion loss up to 40 GHz. Several TRL kits are used to de-embed the effect of connectors and probes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.013
GPT teacher head0.199
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2016
Admission routes1
Has abstractyes

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