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Record W3093551343 · doi:10.1109/ims30576.2020.9224045

Miniaturized Reconfigurable 28 GHz PCM-Based 4-bit Latching Variable Attenuator for 5G mmWave Applications

2020· article· en· W3093551343 on OpenAlexaff
Tejinder Singh, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAttenuator (electronics)ResistorAttenuationMaterials scienceOptoelectronicsExtremely high frequencyOptical attenuatorPlanarMicrofabricationBandwidth (computing)Electrical engineeringElectronic engineeringComputer sciencePhysicsTelecommunicationsEngineeringOpticsFabricationVoltage

Abstract

fetched live from OpenAlex

This paper reports a novel millimeter-wave (mmWave) reconfigurable phase change material (PCM) germanium telluride (GeTe) based 4-bit latching variable attenuator. The proposed variable attenuator is designed using PCM single-pole double-throw (SPDT) switches monolithically integrated with four passive bridged-T resistor network based fixed attenuators to provide wide attenuation range. The PCM switching units are latching type thus consume no static dc power. The integrated planar resistors are fabricated precisely on-wafer to get wide-band operation at desired 8 GHz frequency band. The device is fabricated in-house using a seven-layer microfabrication process. The proposed device is highly miniaturized with device area of 0.52 mm2. At the centre frequency of 28 GHz, the measured attenuation level varies from 4.7 dB to 37 dB with 16 discrete steps. The attenuator can be reconfigured at a tuning speed of less than 1µs,

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.263
Teacher spread0.223 · 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

Citations28
Published2020
Admission routes1
Has abstractyes

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