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Record W2080424997 · doi:10.1143/jjap.46.6503

Radio Frequency Characteristics of Multifinger 0.1 µm Metamorphic High-Electron-Mobility Transistors Depending on Number of Gate Fingers and Gate Width

2007· article· en· W2080424997 on OpenAlexfundno aff
Jung‐Hun Oh, Min Han, Sung‐Woon Moon, Seok-Gyu Choi, Yong‐Hyun Baek, Jin‐Koo Rhee, Sam-Dong Kim

Bibliographic record

VenueJapanese Journal of Applied Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransconductanceCutoff frequencyCapacitanceOscillation (cell signaling)TransistorConductanceMaterials scienceOptoelectronicsMetal gateElectrical engineeringAnalytical Chemistry (journal)ChemistryPhysicsCondensed matter physicsGate oxideElectrodeVoltageEngineering

Abstract

fetched live from OpenAlex

We investigate the effects of the number of gate fingers (N) and gate width (W) on the high-frequency characteristics of 0.1 µm depletion-mode metamorphic high-electron-mobility transistors (MHEMTs). The extracted gate-to-source capacitance (Cgs), gate-to-drain capacitance (Cgd), intrinsic transconductance (gm,int), and drain conductance (Gds) are proportional to total gate width (wt), whereas intrinsic resistance (Ri) and source resistance (Rs) are inversely proportional to wt. Gate resistance (Rg) linearly increases at various slopes with non-zero gate resistances at zero gate width depending on N. The cutoff frequency ( fT) and maximum frequency of oscillation ( fmax) are calculated using a small-signal model and curve-fitting equations extracted from each small-signal parameter. fT is almost constant; however, fmax is a strong function of Rg1/2 and is affected by both N and wt. A large wt produces a low fmax; however, at a given wt, increasing the number of gate fingers is more efficient than increasing single gate width for maximizing the fmax.

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)0.001
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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.232
Teacher spread0.222 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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