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Record W2096011377 · doi:10.1149/1.2728799

NBTI Mechanism Based on Hole-Injection for Accurate Lifetime Prediction

2007· article· en· W2096011377 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2007
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsNegative-bias temperature instabilityMaterials scienceAccelerationInsulator (electricity)Stress (linguistics)Hot-carrier injectionDegradation (telecommunications)InstabilityMOSFETVoltageOptoelectronicsMechanicsElectronic engineeringElectrical engineeringTransistorEngineeringPhysics

Abstract

fetched live from OpenAlex

The evaluation method of Negative Bias Temperature Instability (NBTI) based on hole-injection is proposed for accurate lifetime prediction of MOS devices. The acceleration parameters are most important for accurate lifetime prediction. Proposed acceleration parameter is not the applied voltage to the gate insulator film and the temperature but the injected hole injection quantity to the gate insulator film. The degradation mechanism in the excessive voltage and excessive temperature stresses are different from that in the operation conditions. In the relatively high hole-injection stress, the mechanism is also different from that in the operation conditions. It is considered that the difference is caused by the excess enhancement of the hole energy in the inversion layer. In the relatively low hole-injection stress, the degradation mechanism becomes the same as that in the operation conditions. The accurate lifetime prediction in MOSFET level and circuit level can be realized by hole-injection acceleration method.

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.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.538

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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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