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Record W2913625701 · doi:10.1109/mwscas.2018.8624102

Reduced-Size On-Wafer Inductors using Slow Wave Techniques

2018· article· en· W2913625701 on OpenAlexaff
Ahmad Eldahshan, William Knisely, Rony E. Amaya, Calvin Plett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsCarleton University
Fundersnot available
KeywordsInductanceInductorTransmission lineMicrostripCharacteristic impedanceMaterials scienceElectronic engineeringCoplanar waveguideElectrical impedanceCMOSElectric power transmissionWaferScattering parametersReduction (mathematics)Parasitic capacitanceCapacitancePropagation constantOptoelectronicsElectrical engineeringComputer scienceEngineeringPhysicsMicrowaveMathematicsTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

This paper introduces a study on miniaturizing on-chip RF inductors using the Slow Wave Transmission Line (SWTL) technique. The inductance of different SWTLs is calculated, simulated, fabricated and measured. The lines are treated as two-port networks where their S-parameters and ABCD parameters are extracted, from which their self-inductance is determined. The simulated results have shown a significant reduction in the coplanar microstrip transmission line length reaching 73% with a constant inductance, characteristic impedance, and electrical length. The idea was validated practically by implementing different SWTLs using CMOS 130nm process. The measured and simulated lines are compared and they have shown matched results. Practically the length reduction has reached 30% due to the constraints imposed by the process design rules on the design.

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: none
Teacher disagreement score0.523
Threshold uncertainty score0.876

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 head0.040
GPT teacher head0.248
Teacher spread0.209 · 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
Published2018
Admission routes1
Has abstractyes

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