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Record W2166240511 · doi:10.1109/ccece.2009.5090297

MOSFET model assessment for submicron and nanometer bulk-driven applications

2009· article· en· W2166240511 on OpenAlexafffund
Shaoxi Wang, Rui He, Lihong Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMOSFETElectronic engineeringPower MOSFETComputer scienceLogic gateElectrical engineeringVoltageTransistorEngineering

Abstract

fetched live from OpenAlex

Bulk-driven MOSFET technique meets the low-voltage and low-power requirements demanded in the modern analog circuit design. Due to submicron/nanometer technologies and critical short-channel effects, choosing a suitable MOSFET model for circuit design becomes increasingly important. However, the conventional MOSFET models normally set up for the typical gate-driven applications may not perform correctly and accurately for the bulk-driven applications in the advanced technologies. In this paper, three most widely used MOSFET models, including BSIM, EKV, and PSP, have been extracted for the modern technologies and used in the simulation of bulk-driven applications. Measurement data of fabricated devices are compared with simulation results from distinct models. Several critical MOSFET parameters have been chosen to compare and analyze MOSFET characteristics. The experimental results demonstrate the advantages of the bulk-driven technique compared with the gate-driven scheme. Finally, the performance of distinct MOSFET models is summarized in order to provide analog circuit designers with practical directives.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.354

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)0.0000.000

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2009
Admission routes2
Has abstractyes

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