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Record W2011902862 · doi:10.1143/jjap.39.2203

Study of Optimization Guidelines on Nitrogen Concentration in Nitrided Oxide for Logic and Dynamic Random Access Memory Application

2000· article· en· W2011902862 on OpenAlexfundno aff
Jongwan Jung, Sung-Kye Park, Gyu-Han Yoon, Dae-Kwan Kang, Young‐Jong Lee

Bibliographic record

VenueJapanese Journal of Applied Physics · 2000
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsnot available
FundersGlaucoma Research Society of Canada
KeywordsDramDynamic random-access memoryGate oxideMaterials scienceOptoelectronicsDegradation (telecommunications)TransistorNitridingOxideLogic gateAND gateElectronic engineeringNitrogenMOSFETChannel (broadcasting)Computer scienceElectrical engineeringSemiconductor memoryNanotechnologyComputer hardwareVoltageChemistryEngineeringLayer (electronics)

Abstract

fetched live from OpenAlex

In this paper, we report on the issues and optimization guidelines for nitrided oxide (NO) gates for logic and dynamic random access memory (DRAM) devices. We intensively studied the dependence of device characteristics on the nitrogen concentration in NO gates. In the case of logic devices, degradation of short channel characteristics in n-channel metal oxide semiconductor field effect transistors (NMOSFETs) and degradation of drive current in p-channel MOSFETs (PMOSFETs) limits the maximum nitrogen concentration. In DRAMs, an additional V T dose to compensate for the decrease of V T in NO gates causes a degradation in the refresh time performance due to the increase of the electric field in the space-charge region. This reveals that, for NO-gate DRAM, the nitrogen concentration should be minimized to prevent the degradation of refresh characteristics.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.336

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 head0.026
GPT teacher head0.291
Teacher spread0.265 · 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
Published2000
Admission routes1
Has abstractyes

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