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Record W2117316251 · doi:10.1109/led.2011.2142412

On the Origin of Gate-Induced Floating-Body Effect in PD SOI p-MOSFETs

2011· article· en· W2117316251 on OpenAlexaff
Chih-Hao Dai, Ting‐Chang Chang, Yuan-Jui Kuo, Fu-Yen Jian, Wen-Hung Lo, Szu-Han Ho, Ching-En Chen, Wan-Lin Chung, Jou-Miao Shih, Guangrui Xia, Osbert Cheng, Cheng-Tung Huang

Bibliographic record

VenueIEEE Electron Device Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQuantum tunnellingSilicon on insulatorElectronMOSFETSiliconNegative-bias temperature instabilityMaterials scienceOptoelectronicsAnodeGate oxideCondensed matter physicsOxideElectrical engineeringChemistryPhysicsVoltageTransistorEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

This letter systematically investigates the origin of gate-induced floating-body effect (GIFBE) in partially depleted silicon-on-insulator p-type MOSFETs. The experimental results indicate that GIFBE causes a reduction in the electrical oxide field, leading to an underestimate of negative-bias temperature instability degradation. This can be partially attributed to the electrons tunneling from the process-induced partial n+polygate. However, based on different operation conditions, we found that the dominant origin of electrons was strongly dependent on holes in the inversion layer under source/drain grounding. This suggests that the mechanism of GIFBE at higher voltages is dominated by the proposed anode electron injection model, rather than the electron valence band tunneling widely accepted as the mechanism for n-MOSFETs.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.021
GPT teacher head0.233
Teacher spread0.212 · 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 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

Citations19
Published2011
Admission routes1
Has abstractyes

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