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Record W2166053743 · doi:10.1109/imw.2010.5488403

High performance 65nm 2T-embedded Flash memory for high reliability SOC applications

2010· article· en· W2166053743 on OpenAlexaff
Sung-Rae Kim, Kyung Joon Han, Kin-Sing Lee, Rophina Li, Jonathan Wolfman, Taehoon Kim, Patty Liu, Hyuk Kim, Poong-Yeub Lee, Yu Wang, Yingbo Jia, Fethi Dhaoui, Frank Hawley, Huan-Chung Tseng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlash (photography)Reliability (semiconductor)Flash memoryTransistorNAND gateLeakage (economics)Logic gateCMOSComputer scienceMaterials scienceElectronic engineeringEmbedded systemComputer hardwareElectrical engineeringEngineeringVoltagePower (physics)Physics

Abstract

fetched live from OpenAlex

High speed array architecture and cell optimization in the Uniform Channel Program and Erase (UCPE) floating gate 2 transistor (2T)-embedded flash cell (eFlash) are investigated. It is important to optimize select gate (SG) channel length from 2T-eFlash test array when CG flash device width/length and SG length are pre-determined by other constraints. SG-punch through (PT) driven Gate Disturb (GD) and Gate Induced Drain Leakage current (GIDL) driven GD must be simultaneously studied to determine the optimum CG and SG spacing. For the applications requiring thick tunnel oxide (10nm) such as automotive products, the conductivities of the Sector Select Gate (SSG) device and Control Gate (CG) flash device are critical for read performance. A double SSG scheme per sector and common metal source line architecture were introduced. In this paper, we report our investigation results to optimize 2T eFlash cell design and array architecture to achieve high performance eFlash operation without sacrificing reliability within the constraint of embedding a flash process in the 65nm standard logic process.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.886

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)0.0010.000

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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 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

Citations4
Published2010
Admission routes1
Has abstractyes

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