MétaCan
Menu
Back to cohort
Record W4285133403 · doi:10.1109/tcad.2022.3174165

Accurate Simulation of High-Gain MMIC Amplifiers With Microstrip-Type Transistors

2022· article· en· W4285133403 on OpenAlexaff
Mingye Fu, Nianhua Jiang, Jens Børnemann, Quanyuan Feng

Bibliographic record

VenueIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsNational Research Council CanadaHerzberg Institute of AstrophysicsUniversity of Victoria
FundersSichuan Province Science and Technology Support ProgramChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsMonolithic microwave integrated circuitTransistorMicrostripAmplifierSchematicElectronic engineeringIntegrated circuitElectrical engineeringEngineeringCMOSVoltage

Abstract

fetched live from OpenAlex

A discrepancy of monolithic microwave-integrated circuit (MMIC) amplifier simulations is discussed for conventional electromagnetic (EM) circuit co-simulation in advanced design system (ADS). An obvious discrepancy occurs when microstrip (MS) type transistor schematic models are used in a high gain MMIC amplifier. The coupling effect of a backside via hole in the MS-type model, which is excluded from EM layout simulation, is demonstrated to be the root of this error that will become significant in high-gain MMIC amplifier design. A new method is proposed to include a source via hole of the MS-type transistor in MMIC layout EM simulations. Simulation experiments confirm that this new method can significantly improve the EM simulation accuracy when including a transistor source via hole in the circuit layout EM simulation. The intercoupling effect of the transistor source via is investigated on the correlation with frequency, current gain, via separation distance, and substrate thickness.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.207
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 designSimulation or modeling
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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Computer-Aided Design of Integrated Circuits and SystemsSame topicMicrowave Engineering and WaveguidesFrench-language works237,207