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Parametric 3D Full Wave EM Study for Designing Optimized Phase-Change RF Switches

2023· article· en· W4408717414 on OpenAlexaff
Tejinder Singh, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsParametric statisticsRadio frequencyPhase changePhase (matter)Computer scienceElectronic engineeringMaterials sciencePhysicsTelecommunicationsEngineeringMathematicsEngineering physicsStatistics

Abstract

fetched live from OpenAlex

This paper focuses on using a 3D full wave EM field solver to accurately modeling and predicting the radio frequency (RF) performance by coupling thermal characteristics of the phase-change material and optimizing various design dimensions. A fabricated RF switch is demonstrated with the performance matching closely with the full-wave 3D EM model. An optimal RF performance is achieved through accurate design and careful characterization of the micro-fabrication process. Parametric studies are carried out on a phase-change series single-pole single-throw switch model. The switch is designed with conformal coverage of the materials to represent actual fabricated device. In EM modeling, the material properties were taken by experimentally extracting various characteristics including but not limited to conductivity, sheet resistance, melting point, thermal expansion, stresses to name a few.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.210
GPT teacher head0.352
Teacher spread0.142 · 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.

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
Published2023
Admission routes1
Has abstractyes

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