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Advances of Gate Stack Technology in MOSFETs

2022· article· en· W4317383119 on OpenAlexaff
Lefu Xie

Bibliographic record

Venue2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) · 2022
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStack (abstract data type)MOSFETElectronic engineeringChannel (broadcasting)Electrical engineeringLogic gateDrain-induced barrier loweringDiodeThreshold voltageMaterials scienceComputer scienceOptoelectronicsEngineeringVoltageTransistor

Abstract

fetched live from OpenAlex

In this digital era, an increasing number of semiconductor manufacturing companies have developed thinner and cheaper circuit components, such as diodes, MOSFETs, and FinFETs. At the same time, gate stack technology is also introduced in MOSFETs to help solve the problem posed by short channel effects. There are a large number of researches on different MOSFETs using gate stack technology, therefore, this paper is a literature review of three different kinds of gate stacked MOSFETs and their behavior against short channel effects. The MOSFETs discussed in this paper are investigated in 2017, 2018 and 2020, and the ability of weakening short channel effects is also analyzed. Drain-induced barrier lowering (DIBL), threshold voltage roll-off, and subthreshold swing are three common short channel effects, and though the improvement in gate stack technology, they are suppressed in some degree. Gate stack technology has been developing new possibilities and it is expected that gate stack MOSFETs are much more powerful in confronting short channel effects and they will play a key role in different fields in the future.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.254
Teacher spread0.234 · 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
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
Published2022
Admission routes1
Has abstractyes

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